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In this section we study the generalization of our features on downstream classification benchmarks. We
consider two sets of evaluations in that context. On one hand, we use large and finegrained datasets such
as iNaturalist and Places205. On the other, we use the 12 image classification tasks originally proposed
in SimCLR (Chen et al., 2020). For iNaturalist 2018, iNaturalist 2021, and Places205, we train a linear
classifier with data augmentations as in Sec. 7.1 We report top-1 accuracy for those three datasets in Table 7.
Interestingly, our model significantly outperforms OpenCLIP ViT-G/14 on both variants of iNaturalist
(+8.6% and +9.7% for 2018 and 2021 respectively), and lags slightly behind on Places 205 (−2.3%).
In a second set of evaluations, we measure the performance of our model on video action recognition even
though our features were not trained on videos.. We evaluated features on three datasets, namely UCF- | DINOv2- Learning Robust Visual Features without Supervision |
Continual learning. Recent studies [190; 272] have highlighted the potential of LLMs’ planning
capabilities in facilitating continuous learning [196; 197] for agents, which involves continuous
acquisition and update of skills. A core challenge in continual learning is catastrophic forgetting
[273]: as a model learns new tasks, it tends to lose knowledge from previous tasks. Numerous efforts
have been devoted to addressing the above challenge, which can be broadly separated into three
groups, introducing regularly used terms in reference to the previous model [274; 275; 276; 277],
approximating prior data distributions [278; 279; 280], and designing architectures with task-adaptive
parameters [281; 198]. LLM-based agents have emerged as a novel paradigm, leveraging the planning
capabilities of LLMs to combine existing skills and address more intricate challenges. Voyager [190]
attempts to solve progressively harder tasks proposed by the automatic curriculum devised by GPT-4 | TheRiseandPotentialofLargeLanguageModel BasedAgents |
transcriptions. Individual samples of the AMI dataset contain very large audio files between 10
and 60 minutes in duration. We segment the audio samples according the the Kaldi (Povey et al.,
2011) recipe for AMI3 to yield utterance of suitable length for training ASR systems. This involves
splitting samples longer than 30 words at the time-stamps for punctuation to yield shorter utterances.
We use the individual headset microphone (AMI IHM) and single distant microphone (AMI SDM)
versions of the dataset, with the train, validation and test sets provided therein. | DISTIL-WHISPER |
Table 10: Qualitative examples from WebNLG. The first 6 examples are from the unseen categories, labeled next
to source; the last two examples are from the seen categories. For unseen categories, both prefix-tuning and fine-
tuning tend to undergenerate (generated output do not cover full table contents) or generate untruthfully (generated
output is inconsistent with table contents). In particular, prefix-tuning tends to undergenerate more often than
generate untruthfully whereas fine-tuning tends to generate untruthfully. For seen categories, both perform fairly
well in terms of coverage and truthfulness.
4597 | Prefix-Tuning |
led model training and evaluation for controlled sentiment generation and summarization; design
iterations for GPT-4 evaluation (particularly summarization); substantial writing contributions to
abstract, prelims/method and experiments; editing contributions to other sections.
EM provided input on early discussions on learning autoregressive reward functions; wrote the first
implementation of DPO and ran the first DPO experiments; trained the large-scale (summarization
and dialogue) DPO models used in paper experiments; conducted initial GPT-4 win rate evaluations
and set up related infrastructure; recruited participants for, conducted, and analyzed results from the
human study; wrote the abstract, introduction, related work, discussion, and most of experiments;
and assisted with editing the rest of the paper.
CF, CM, & SE supervised the research, suggested ideas and experiments, and assisted in writing the
paper. | Direct Preference Optimization |
the behavior of LLMs.
5. Experts are not yet able to interpret the inner
workings of LLMs.
6. Human performance on a task isn’t an upper
bound on LLM performance.
7. LLMs need not express the values of their
creators nor the values encoded in web text.
8. Brief interactions with LLMs are often mis-
leading.
Introduction
Large language models (LLMs, e.g. GPT-3, PALM,
LLaMA, and GPT-4; Brown et al., 2020; Chowdhery et al.,
2022; Touvron et al., 2023; OpenAI, 2023b) and products
built on them, such as ChatGPT, have recently prompted
an enormous amount of attention from journalists, (Klein,
2023; Perrigo, 2023; Oliver, 2023), policymakers (J & C,
2023; Bartz, 2023; Lieu, 2023), and scholars from many
1New York University 2Anthropic, PBC. Correspondence to:
Samuel R. Bowman <[email protected]>. | Eight Things to Know about Large Language Models |
6 CONCLUSION AND FUTURE CHALLENGES
Recent advances in large language models have been revolutionizing the field of natural language processing. Effectively
using LLMs requires understanding their capabilities, and limitations for various NLP tasks. This work presents a
practical guide to working with LLMs for downstream NLP tasks. We first discuss prominent models like GPT-style and
BERT-style architectures and the factors influencing their performance. We then explore using LLMs for downstream
tasks, including knowledge-intensive tasks, NLU, and NLG tasks, as well as providing concrete examples of successes
and limitations. This practical guide offers insights into LLMs and best practices for harnessing LLMs across NLP tasks.
We hope it would enable researchers and practitioners to leverage their potential and drive innovation in language
technologies. | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
• The volume of data in Delta Lake
has grown 304% YoY
• The Lakehouse is increasingly
being used for data warehousing,
including serverless data
warehousing with Databricks
SQL, which grew 144% YoY
6
2023 STATE OF DATA + AI Methodology: How did Databricks
create this report?
The 2023 State of Data + AI is built from fully-aggregated, anonymized data
collected from our customers based on how they are using the Databricks
Lakehouse and its broad ecosystem of integrated tools. This report focuses
on machine learning adoption, data architecture (integrations and migrations)
and use cases. The customers in this report represent every major industry
and range in size from startups to many of the world’s largest enterprises.
Unless otherwise noted, this report presents and analyzes data from February 1,
2022, to January 31, 2023, and usage is measured by number of customers.
When possible, we provide YoY comparisons to showcase growth trends over time. | 2023 state of ai databrick |
elements: 1) an encoder which learns a feature representation of the inputs using
two layers of Transformers and 2) a decoder which combines the last predicted
note and the encoded representation as input and feeds them to one unidirec-
tional LSTM to produce the final output which is the predicted next note. They
demonstrated from a listening test that generated music pieces from their pro-
posed model are rated as good as or better than the music pieces from human
composers.
In very recent work, Transformer architectures have also been used in Diffu-
sion networks for monophonic symbolic music generation (Mittal et al., 2021),
8
which further shows their ability to model music.
In this work, we will be conditioning our Transformer network on video
features. While many Transformer-based music generation models primarily
focus on generating MIDI files, our proposed model generates chord sequences | Video2Music |
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| Language models can explain neurons in language models |
of knowledge and needs, ethical concerns, and the impersonal interaction. | Adoptionand AppropriationofLLMs |
In music composition, the arrangement of a piece
typically follows a gradual introduction, a main
body with the core content, and a gradual conclu-
sion, also called the sonata form (Webster, 2001).
Accordingly, we look into whether our generated
music also shows such a long-term structure. Us-
ing the same text prompt, we can generate different
segments/intervals of it by attaching the expression
“1/2/3/4 out of 4” at the end of the text prompt, such
as “Italian Hip Hop 2022, 3 of 4.” We randomly
generate 1,000 music pieces, where the prompts
are from a uniform distribution of the four segment
tags. We visualize the results in Figure 6, where
we see the first segment shows a gradual increase
in both the average amplitude and variance, fol-
lowed by continuously high average amplitude and
variance throughout Segments 2 and 3, and finally
concluding with a gradual decline in the last seg-
ment. | MOUSAI |
consistent motion as opposed to the 1B model 5 roses and
distorting objects produced by the 1B model. Overall, scal-
ing the model improved temporal consistency, prompt fi-
delity, and motion dynamics while adding capabilities for
limited text rendering, spatial understanding, and counting.
A.4. Stylization Evaluation on DAVIS
To evaluate the CLIP similarity score and human preference
on video stylization, we use the following set of videos and
prompts. We select 20 videos from DAVIS 2016 [43], and
for each video we take 16 frames starting from the initial | VideoPoet |
We represent each API call as a tuple c = (ac, ic)
where ac is the name of the API and ic is the cor-
responding input. Given an API call c with a cor-
responding result r, we denote the linearized se-
quences of the API call not including and including
its result, respectively, as:
e(c) = <API> ac(ic) </API>
e(c, r) = <API> ac(ic) → r </API> | Toolformer |
[80] Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word
2021.
representations. arXiv, 2018.
[81] Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. Is chatgpt a general-purpose natural language
[82] Shilin Qiu, Qihe Liu, Shijie Zhou, and Wen Huang. Adversarial attack and defense technologies in natural language processing: A survey.
processing task solver? arXiv preprint arXiv:2302.06476, 2023.
Neurocomputing, 492:278–307, 2022.
[83] Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring,
Susannah Young, et al. Scaling language models: Methods, analysis & insights from training gopher. arXiv preprint arXiv:2112.11446, 2021.
[84] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Here, concerns about balancing Type 1 and Type
2 errors disappear. Preregistration mitigates risks
associated with research, reducing potential harms,
but at the cost of scientific progress. This calls
for a cost-benefit analysis: How much risk can be
tolerated for what potential gains? | A Two-Sided Discussion of Preregistration of NLP Research |
F.4 Ablations
In Table 18, we report key-retrieval accuracy for ablations performed on an earlier version of our 7B model.
Without long context fine-tuning, retrieval is possible on sequence lengths seen during training only (4,096);
increasing RoPE’s base period θ for inference only has no effect here. Performing LCFT without changing the
base period results in failure to retrieve far-away keys at a context length of 8,000 already, despite fine-tuning
with a 16,384 sequence length. This failure suggests that adapting the rotation frequencies is indeed necessary.
We evaluate frequency scaling with a factor of 1/4 (Chen et al., 2023b), corresponding to the 4x increase of
sequence length during fine-tuning. Retrieval performance at 16,00 tokens for keys placed at the beginning is
low in this configuration, and extrapolation to longer sequences fails.
G Prompts
G.1 Self training prompts | CodeLlama2 |
3 STABILIZING TRAINING OF SPARSE MODELS
Sparse models often suffer from training instabilities (Figure 1) worse than those observed in stan-
dard densely-activated Transformers.
Figure 1: Training instabilities for sparse models. We refer to training instabilities as divergences
in the training loss. Above are two runs from sparse models FLOP-matched to the T5-XL version
(Raffel et al., 2019) each trained with a batch size of 1M tokens using the Adafactor optimizer
(Shazeer and Stern, 2018). (Left) An unstable training run. (Right) A stable training run. | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
A.3.2 Curriculum Strategy for Meta Human Preference Data
High quality data is critical for alignment as discussed for SFT. We worked closely with the annotation
platforms during our fine-tuning process, and opted for a curriculum annotation strategy. With the first
model, the annotators were asked to make prompts relatively simple, and then to progressively move towards
more complex prompts and teaching new skills to Llama 2-Chat. An illustration of this curriculum annotation
on our helpfulness preference data is displayed in Figure 26. | Llama2 |
modality generation quality using widely available modality-specific training data (i.e., data with one
or more modalities as input and one modality as output). For conditional cross-modality generation,
such as generating images using audio+language prompts, the input modalities are projected into a
shared feature space (Section 3.2), and the output LDM attends to the combination of input features.
This multimodal conditioning mechanism prepares the diffusion model to condition on any modality
or combination of modalities without directly training for such settings.
The second stage of training enables the model to handle many-to-many generation strategies that
involve simultaneously generating arbitrary combinations of output modalities. To the best of our
knowledge, CoDi is the first AI model with this capability. This is achieved by adding a cross-
attention module to each diffuser, and an environment encoder V to project the latent variable of | Any-to-Any Generation via Composable Diffusion |
7 System design
System design is critical in optimizing Large Language Models (LLMs) like the GPT
series for efficient inference, particularly in resource-constrained environments. This
section explores key strategies such as hardware offloading, which manages computa-
tional resources by leveraging different storage hierarchies, and collaborative inference,
which pools resources for enhanced processing capabilities. It also examines the adap-
tation of LLMs for edge devices, highlighting the importance of system design in
maximizing the efficiency and scalability of LLMs across various deployment scenarios.
7.1 Deployment optimization | Beyond Efficiency |
4.1 Methodology
To ensure a fair comparison across datasets of dif-
ferent sizes, we decontaminate any instances of the
evaluation sets using the same 13-gram overlap fil-
tering as in Brown et al. (2020) and downsample
to 40GB to control for dataset size. As we control
for dataset size, we emphasize that our evaluation
is generous to CC-100 (en), which is about 1/3 the
size of the Pile in reality.
We compare the following datasets: the Pile, the En-
7
Component
GPT-2
GPT-3
Pile-CC
PubMed Central
Books3
OpenWebText2
ArXiv
Github
FreeLaw
Stack Exchange
USPTO Backgrounds
PubMed Abstracts
Gutenberg (PG-19)
OpenSubtitles
Wikipedia (en)
DM Mathematics
Ubuntu IRC
BookCorpus2
EuroParl
HackerNews
YoutubeSubtitles
PhilPapers
NIH ExPorter
Enron Emails
The Pile
small
1.0878
1.0759
1.1959
1.1111
1.3548
1.7912
1.0512
1.2981
0.8288
0.9524
1.2655
1.2465
1.1285
2.6911
1.8466
1.1295
2.3177
1.4433
2.0387
1.3203
0.9099
1.5888
1.2253 | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
5 Limitations
Although MiniGPT-4 processes numerous advanced vision-language capabilities, as displayed in our
demonstrations, it currently still faces several limitations.
Language hallucination. As MiniGPT-4 is built upon LLMs, it inherits LLM’s limitations like
unreliable reasoning ability and hallucinating nonexistent knowledge. This issue might be alleviated
5
Figure 2: Detailed image descriptions | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
Concrete problems in ai safety.
[Askell et al., 2021] Askell, A., Bai, Y., Chen, A., Drain, D., Ganguli, D., Henighan, T., Jones, A., Joseph,
N., Mann, B., DasSarma, N., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Kernion, J., Ndousse, K.,
Olsson, C., Amodei, D., Brown, T., Clark, J., McCandlish, S., Olah, C., and Kaplan, J. (2021). A general
language assistant as a laboratory for alignment.
[Bender et al., 2021] Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021). On the
dangers of stochastic parrots: Can language models be too big? ￜￜ. In Proceedings of the 2021 ACM
Conference on Fairness, Accountability, and Transparency, FAccT ’21, pages 610–623, New York, NY,
USA. Association for Computing Machinery. | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
y
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int
int
pos
pos
4.3. Recommender systems
Knowledge graphs to provide more transparent results to models’ outputs have recently experienced a take-up also in
the area of recommender systems, with the goal of enhancing the users’ experience in terms of satisfaction, trust, and
loyalty. Most of the approaches are content-based, i.e. they consists of explaining a recommendation with entities from a
given knowledge graph in the form of images or natural language sentences. | Knowledge graphs as tools for explainable machine learning: A survey |
sha1_base64="0Q3PNdwUTyjvy3/Zd46cnh2h4C0=">AAACAHicbVDLSsNAFJ34rPUVdeHCzWARqouSiKDLghuXFexDmhgm00k7dGYSZiZCCdn4K25cKOLWz3Dn3zhps9DWAxcO59zLvfeECaNKO863tbS8srq2Xtmobm5t7+zae/sdFacSkzaOWSx7IVKEUUHammpGeokkiIeMdMPxdeF3H4lUNBZ3epIQn6OhoBHFSBspsA89RYccwbrHkR6FUdbLA/pwdhrYNafhTAEXiVuSGijRCuwvbxDjlBOhMUNK9V0n0X6GpKaYkbzqpYokCI/RkPQNFYgT5WfTB3J4YpQBjGJpSmg4VX9PZIgrNeGh6SzOVPNeIf7n9VMdXfkZFUmqicCzRVHKoI5hkQYcUEmwZhNDEJbU3ArxCEmEtcmsakJw519eJJ3zhus03NuLWvO+jKMCjsAxqAMXXIImuAEt0AYY5OAZvII368l6sd6tj1nrklXOHIA/sD5/AM9glfQ=</latexit><latexit | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
cleaning [54, 60]. Training for Aesthetics and CLIP im-
proves those capabilities more specifically, in the case of
Aesthetics at the expense of CLIP. The ability to train for
text-image alignment via CLIP is a noted improvement over
prior work [7]. Moreover, training SD1.5 on the pseudo-
labeled PickScore dataset (β = 5000, 2000 steps) outper-
forms training on the raw labels. On the General Preference
Partiprompt question, the win-rate of DPO increases from
59.8% to 63.3%, indicating that learning from AI feedback
can be a promising direction for diffusion model alignment.
5.5. Analysis
Implicit Reward Model As a consequence of the theo-
retical framework, our DPO scheme implicitly learns a re-
ward model and can estimate the differences in rewards be-
tween two images by taking an expectation over the inner
term of Eq. (14) (details in Supp. S4.1). We estimate over
10 random t ∼ U{0, 1} Our learned models (DPO-SD1.5 | DiffusionModelAlignmentUsing Direct Preference Optimization |
Katja Grace et al. “Viewpoint: When Will AI Exceed Human Performance? Evidence
from AI Experts”. en. In: Journal of Artificial Intelligence Research 62 (July 2018),
pp. 729–754. ISSN: 1076-9757. DOI: 10.1613/jair.1.11222. URL: http://jair.org/index.
php/jair/article/view/11222 (visited on 04/29/2022).
Katja Grace. Misalignment and misuse: whose values are manifest? en-US. Section:
Blog. Nov. 2020. URL: https://aiimpacts.org/misalignment-and-misuse-whose-values-
are-manifest/ (visited on 04/29/2022).
Joseph Henrich. The Secret of Our Success: How Culture Is Driving Human Evolution,
Domesticating Our Species, and Making Us Smarter. English. Princeton: Princeton
University Press, Oct. 2015. ISBN: 978-0-691-16685-8.
Evan Hubinger. Clarifying inner alignment terminology - AI Alignment Forum. URL:
https:// www.alignmentforum.org/posts/SzecSPYxqRa5GCaSF /clarifying- inner-
alignment-terminology (visited on 04/29/2022). | Is Power-Seeking AI an Existential Risk? |
sample N p = 6144 pixels from all image pairs for render-
ing. The interval between image pairs is randomly chosen
∆T ∈ {1, 2, 4, 8, 16, 32}. To stabilize optimization, we ob-
serve that NI needs to roughly match the number of input
frames. The reconstruction quality improves with more iter-
ations and we find 36k iterations (15 hours on a V100 GPU)
already produces high-fidelity details. Please find a list of
hyper-parameters in supplement. | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
sha1_base64="/NxVbjiSFkKRfDP6dqe151Iuji8=">AAAB+HicbVDLSgNBEOz1GeMjqx69DAYhXsKuCHoMePEYwTwkiWF2MpsMmX0w0yvGJV/ixYMiXv0Ub/6Ns8keNLFgoKjqpmvKi6XQ6Djf1srq2vrGZmGruL2zu1ey9w+aOkoU4w0WyUi1Paq5FCFvoEDJ27HiNPAkb3njq8xvPXClRRTe4iTmvYAOQ+ELRtFIfbvEKt2A4sjz08fpPZ727bJTdWYgy8TNSRly1Pv2V3cQsSTgITJJte64Toy9lCoUTPJpsZtoHlM2pkPeMTSkAde9dBZ8Sk6MMiB+pMwLkczU3xspDbSeBJ6ZzELqRS8T//M6CfqXvVSEcYI8ZPNDfiIJRiRrgQyE4gzlxBDKlDBZCRtRRRmaroqmBHfxy8ukeVZ1nap7c16u3eV1FOAIjqECLlxADa6hDg1gkMAzvMKb9WS9WO/Wx3x0xcp3DuEPrM8fmWuTHA==</latexit><latexit | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
prompt for a pre-trained text-to-video model. Our approach
has the following appealing advantages:
• Instruction-Followed Video Understanding: The pro-
posed GPT4Video effectively harnesses the robust con-
textual summarization and textual expression capabilities
of LLM to generate detailed prompts for videos, with
such detail-rich prompts proven to be crucial for the out-
comes of generative models [16]. | GPT4Video |
Transparency Reports
Many platforms publish periodic transparency reports, which typically disclose
aggregate data about requests for content removal. An index of transparency
reports maintained by the civil society organization Access Now lists reports
from more than seventy companies,14 including Google,15 Facebook,16
Twitter,17 Amazon,18 Tumblr,19 Medium,20 Reddit,21 Github,22 and
WordPress.23 These can provide important quantitative overviews of the big
picture – or at least part of it. They typically aggregate data about removal
requests, along with the platform’s rate of compliance. They may also disclose
the frequency with which users accused of wrongdoing choose to appeal or
challenge platforms’ decisions. Transparency reports have historically focused
on legal removal requests. In 2018, however, Facebook,24 Twitter,25 and
YouTube26 all published their first Community Guidelines enforcement
reports. | Social_Media_and_Democracy |
4
−4−3−2−1012OutputMagnitude(beforeReLU)CountFalseNegativeUpProjectionPredictorNLow Rank PredictorMMNMRReLUsigmoid
> 0.5Up Projection
(FC)001010...00N= d modelM = dffn (a) aggregated neuron use
(b) sliding window
Figure 4: (a) Aggregated neuron use of the tenth layer of Falcon 7B, as it can be seen the slop of aggregated neuron
use is decreasing. Other layers exhibit the same pattern. (b) Instead of deleting neurons that brought to DRAM we
keep the active neurons of past 5 tokens: when the new token "Was" is being processed only a few amount of data
needs to be changed.
3.2
Improving Transfer Throughput with
Increased Chunk Sizes
To increase data throughput from flash memory, it
is crucial to read data in more substantial chunks.
In this section, we detail the strategy we have em-
ployed to augment the chunk sizes for more effi-
cient flash memory reads. | LLM in a flash |
significant breakthroughs have been achieved in the development of multimodal generative models, e.g. models that
can generate images from text. Technological advancement in this direction will probably have significant influence on
the production and creation of art. Models that can translate data from different modalities into a joint semantic space
represent an interesting tool for artistic exploration because the concept of multimodality is integral to many art forms
and has always played an important role in the creative process. Furthermore, it is evident that the increasing use of
AI technologies in the creation of art will have significant implications regarding the questions related to authorship,
as well as on our human perception of art. With the development of AI models that can generate content which very
convincingly imitates human textual, visual or musical creations, many of our traditional, as well as contemporary, | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
[341] Carlini, N., J. Hayes, M. Nasr, et al. Extracting training data from diffusion models. CoRR,
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[342] Savelka, J., K. D. Ashley, M. A. Gray, et al. Can GPT-4 support analysis of textual data in
tasks requiring highly specialized domain expertise? In F. Lagioia, J. Mumford, D. Odekerken,
H. Westermann, eds., Proceedings of the 6th Workshop on Automated Semantic Analysis of
Information in Legal Text co-located with the 19th International Conference on Artificial
Intelligence and Law (ICAIL 2023), Braga, Portugal, 23rd September, 2023, vol. 3441 of
CEUR Workshop Proceedings, pages 1–12. CEUR-WS.org, 2023.
[343] Ling, C., X. Zhao, J. Lu, et al. Domain specialization as the key to make large language models
disruptive: A comprehensive survey, 2023.
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learning interpretability methods. Entropy, 23(1):18, 2021. | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Other Categories and Types of Hallucinations. Raunak et al. [153] propose an alternative catego-
rization of hallucinations. They divide hallucinations into hallucinations under perturbations and
natural hallucinations. Hallucinations under perturbation are those that can be observed if a model
tested on the perturbed and unperturbed test set returns drastically different content. Their work
on hallucinations under perturbation strictly follows the algorithm proposed by Lee et al. [95]; see
Section 11.2.2 on the entropy measure. The second category, natural hallucinations, are created with
a connection to the noise in the dataset and can be further divided into detached and oscillatory,
where detached hallucinations mean that a target translation is semantically disconnected from
a source input, and oscillatory hallucinations mean those that are decoupled from the source by
manifesting a repeating n-gram. Tu et al. [187] and Kong et al. [87] analyze this phenomenon under | SurveyofHallucinationinNatural Language Generation |
4. code-cushman-001 is a 12B parameter model by OpenAI and was the initial model for
GitHub Copilot (Chen et al., 2021). The details of its training set are unknown. This model
has been deprecated by OpenAI but was available from the Microsoft Azure OpenAI Service
at the time of writing.13
5. Finally, although they are not specifically trained for code generation, we include some
results from the LLaMA (Touvron et al., 2023), PaLM (Chowdhery et al., 2022), and
LaMDA (Thoppilan et al., 2022) papers. LLaMA’s license prohibits commercial use, and
PaLM and LaMDA are not publicly available.
13There had been a code-cushman-002, but it is not available at the time of writing.
17
Model
LLaMA-7B
LaMDA-137B
LLaMA-13B
CodeGen-16B-Multi
LLaMA-33B
CodeGeeX
LLaMA-65B
PaLM-540B
CodeGen-16B-Mono
StarCoderBase
code-cushman-001
StarCoder
StarCoder-Prompted
HumanEval MBPP
17.7
14.8
22.0
20.9
30.2
24.4
37.7
36.8
35.3
49.0
45.9
52.7
49.5 | StarCoder_paper (1) |
<jupyter_start><jupyter_text>TEXT<jupyter_code>CODE
<jupyter_output>OUTPUT<jupyter_text> ...
Git commits We separated the code before the commit, the commit message, and the code after
the commit with sentinel tokens. We included the full code with changes instead of diffs, as early
experiments suggested that the diff format was difficult to output for smaller models. See Section 3.4
for more details.
<commit_before>code<commit_msg>text<commit_after>code<eos>
We summarize all sentinel tokens in Table 10.
5 . 2 T R A I N I N G D ATA D E C O N TA M I N AT I O N
The code training data was decontaminated by removing files that contained docstrings or solutions
from HumanEval and MBPP, docstrings from APPS, questions from GSM8K, or prompts from
DS1000. (These benchmarks are further described in Section 6.) To give an indication of the amount
of data removed by decontamination, Python is the language with the highest number of matches,
with 558 files removed.
5 . 3 T O K E N I Z E R | StarCoder_paper (1) |
Reddit, Inc. (2015). Reddit, Inc. Transparency Report, 2015. www.reddit.com/wiki/
transparency/2015
Roberts, S. T. (2016). Commercial content moderation: Digital laborers’ dirty work.
Media Studies Publications, Paper No. 12. https://ir.lib.uwo.ca/cgi/viewcontent
.cgi?article=1012&context=commpub
(2019). Behind the Screen: Content Moderation in the Shadows of Social Media. New
Haven, CT: Yale University Press.
Seng, D., (2015). “Who watches the watchmen?” An empirical analysis of errors in
SSRN. https://papers.ssrn.com/sol3/papers.cfm?
DMCA takedown notices.
abstract_id=2563202
Taub, A., & Fisher, M. (2018). Facebook fueled anti-refugee attacks in Germany, new
research suggests. New York Times, August 21. www.nytimes.com/2018/08/21/
world/europe/facebook-refugee-attacks-germany.html | Social_Media_and_Democracy |
Does your application use case require rigor, precision and is in a zero-mistakes
allowed environment? Or are you deploying closer to the end consumer with a more
forgiving experience yet the need to offer refreshing thoughts?
While exceptions are always the rule, often fintech founders impress us with a deep
understanding of the problem space and bring relevant niche experience. Mixed
with a strong technical profile, this allows you to bring a quick go-to-market through
nailing the tone as well as providing a technical sound solution.
Lastly, as MOATs are continuously being redefined, teams that are capable of
listening, observing and quickly adapting while also being true to their first-
principles thinking, have the best chances to succeed.
https://medium.com/lightspeed-venture-partners/fintech-x-ai-the-lightspeed-view-b515fae5bfb6
6/15
23/06/2023, 17:55
Fintech x AI: The Lightspeed View | by Lightspeed | Lightspeed Venture Partners | Jun, 2023 | Medium | Fintech x AI_ The Lightspeed View _ by Lightspeed _ Lightspeed Venture Partners _ Jun, 2023 _ Medium |
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As we see above, both improved language model capabilities and limitations can pose significant
challenges to the responsible and safe societal adoption of these models. To ensure that we are all
well-prepared for the pace of progress, we need more research emphasis on areas such as AI literacy,
economic and social resilience, and anticipatory governance.[11] It is very important that OpenAI,
other labs, and academia further develop effective evaluation tools and technical improvements in
model safety. Progress has been made in the last few years, and more investment in safety will likely
produce more gains.
We encourage readers interested in this topic to read our work on language model impacts in
areas such as disinformation, misuse, education, and economy and labor market.
29 | gpt-4-system-card |
5.2 From Tool User to Tool Maker: AI’s Evolutionary Role
Throughout the annals of human civilization, the evolution of tools has occupied a pivotal position (Mithen,
1996; Ko, 2016). The Stone Age, in particular, witnessed the emergence of stone-based weaponry and hunting
tools, which afforded humans a competitive edge over their animal counterparts. Subsequent epochs of human
history were equally marked by significant societal transformations made possible by the introduction of novel
tools. Notably, the invention of the steam engine heralded the onset of the first industrial revolution, while
29
5.2 From Tool User to Tool Maker: AI’s Evolutionary Role
Figure 8: Example of AI tool creation, where we ask ChatGPT to encapsulate a weather forecast API into a
new function suited for a specific target. | Tool Learning with Foundation Models |
resulting in notable advancements across many tasks such as speech recognition and audio QA tasks.
• Output Instruction: Lastly, we provide output instruction to further specify the task and desired format | Qwen-Audio |
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4. “Intelligence explosion”: that is, AI-driven feedback loops lead to explosive growth in
frontier AI capabilities, at least for some period (on my definition, this need not be driven
by a single AI system “improving itself”—see below; and note that the assumption that
feedback loops explode, rather than peter out, requires justification).143
5. “Recursive self-improvement”: that is, some particular AI system applying its capabilities
to improving itself, then repeatedly using its improved abilities to do this more (sometimes
assumed or expected to lead to an intelligence explosion; though as above, feedback loops
can just peter out instead). | Is Power-Seeking AI an Existential Risk? |
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Implications and Broader Context
6
We started with two hypotheses: a) that the emer-
gence of nearly all functional linguistic abilities
that has previously been observed is a consequence
of in-context learning, and b) that the ability of
LLMs to follow instructions when instruction-
tuned is more likely to be indicative of instruc-
tion tuning allowing for the more efficient use of
in-context learning rather than leading to the emer-
gence of reasoning skills. Results presented in Sec-
tion 4 confirmed that there are indeed no emergent
abilities in the absence of in-context learning. Sim-
ilarly, results presented in Section 4.3 confirmed
our second hypothesis. | AreEmergentAbilitiesinLarge Language Models just In-Context |
10 Energy and Carbon Footprint Estimate of LaMDA | LaMDA- Language Models for Dialog Applications |
D.3. Results
After submissions we computed our score on each contest (including penalties) using the contests’
scoring system, and found where the model would have placed on the contests’ official scoreboards.
Per-problem contest results can be found in Table A5. Overall contest results can be found in Table
A6. In the second and third evaluations, we submitted more than 10 submissions per problem. We
found that there were some problems we only solved with many samples.
We also computed our estimated Codeforces Elo score by tracking what our Elo would have been
if we started with the first contest, and competed in each contest in the order they were released,
placing according to our calculated placement in Table A6. This was done separately for all three
evaluations, and then averaged.
Our Elo estimation is based on our reproduction of the Codeforces Elo method, as we didn’t compete
live. We checked correctness by reproducing other participants’ Elo scores. Our approach largely | alphacode |
5/12
14/11/2023, 13:39
The Future of Music: How Generative AI Is Transforming the Music Industry | Andreessen Horowitz
that enables others to create new songs with her voice. She’s pledged to split royalties with any
AI-created song that is able to generate revenue.
TA B L E O F C O N T E N T S
We expect to see infrastructure emerge to support this on a greater scale. For example, artists
need a place to store their custom voice models, track AI covers, and understand streams and
monetization across tracks. Some artists or producers may even want to use their voice models
to test different lyrics, see how a given voice sounds on a song, or experiment with different
collaborators on a track.
Royalty-Free Tracks (aka AI Muzak) | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
Learning conditional controls for large text-to-image dif-
fusion models in an end-to-end way is challenging. The
amount of training data for a specific condition may be sig-
nificantly smaller than the data available for general text-to-
image training. For instance, the largest datasets for various
specific problems (e.g., object shape/normal, human pose
extraction, etc.) are usually about 100K in size, which is
50,000 times smaller than the LAION-5B [79] dataset that
was used to train Stable Diffusion [82]. The direct finetun-
ing or continued training of a large pretrained model with
limited data may cause overfitting and catastrophic forget-
ting [31, 75]. Researchers have shown that such forgetting
can be alleviated by restricting the number or rank of train-
able parameters [14, 25, 31, 92]. For our problem, designing
deeper or more customized neural architectures might be
necessary for handling in-the-wild conditioning images with | AddingConditionalControltoText-to-ImageDiffusionModels |
Figure 2: The final training data was curated to ensure a diverse distribution of prompt topics and model responses.
2.1 Reproducibility
We release all data (including unused P3 genera-
tions), training code, and model weights for the
community to build upon. Please check the Git
repository for the most up-to-date data, training
details and checkpoints.
2.2 Costs
We were able to produce these models with about
four days work, $800 in GPU costs (rented from
Lambda Labs and Paperspace) including several
failed trains, and $500 in OpenAI API spend.
Our released model, gpt4all-lora, can be trained in
about eight hours on a Lambda Labs DGX A100
8x 80GB for a total cost of $100.
3 Evaluation | GPT4All- Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo |
AI Performer and Human Validator. While autonomous AI agents reduce human’s cog-
nitive workload and let them concentrate on other tasks, human (ethical) supervision is
often needed. This design pattern is represented in Table 3 and its implementations are
found in all four use cases. In the personalized care example (Sect. 4.4) a domain expert
supervises the AI interactions with the patient to ensure a stable and safe environment.
In the first response use case (Sect. 4.1) a fire fighter validates that the AI correctly inter-
prets the situation, while in the maintenance scenario (Sect. 4.2) the technician validates
E. van Zoelen et al. /
the result of the suggested repairs. Lastly, in the wildlife monitoring scenario (Sect. 4.3)
an expert validates the visual information provided by the AI and extends it by provid-
ing further annotations. In all four examples, the AI uses the feedback received from the
human actor to improve over time.
Description | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
our use case, i.e., that the weights sum to unity, and there is
no requirement of orthogonality, unlike in PCA. | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
arXiv preprint arXiv:2309.05922, 2023.
Paul Röttger, Hannah Rose Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk
Hovy. Xstest: A test suite for identifying exaggerated safety behaviours in large language models.
arXiv preprint arXiv:2308.01263, 2023.
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi
Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. Code llama: Open foundation models for code.
arXiv preprint arXiv:2308.12950, 2023.
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adversarial winograd schema challenge at scale. arXiv preprint arXiv:1907.10641, 2019.
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine
Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. Multitask prompted training enables
zero-shot task generalization. arXiv preprint arXiv:2110.08207, 2021. | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
and its correction, 182–183
on, 133
Nelson, J. L., 19
net neutrality, 210, 267
Network Enforcement Law (NetzDG), 199,
205, 230, 232–234, 299–300
neutrality of internet platforms in relationship
to users’ speech, 223–224
The New Governors (Klonick), 238
New York Times Co. v. Sullivan, 262
Newell, Edward, 72
news bots, 96–97
news media
attention shift away from news, 144
consequences of changes in, 157
expansion of news sources to individuals and
organizations, 146–147
impact on democracy, 139–158
individual-level changes in, 148–155
institutional changes in, 142–148
loss of trust in, 153
online harassment, 154
operational changes, 146
structural changes and impact on democracy,
139–141
newspapers, 143, 204
n-grams method, hate speech detection, 59
Nielsen, Rasmus Kleis, 40
Nimmo, B., 99
Nora, Simon, 207
notice and takedown systems, 222, 226–227,
230, see also content takedown | Social_Media_and_Democracy |
4.2 Confirmatory Factor Analysis (CFA)
Fig. 2. The findings of the confirmatory factor analysis indicated a two-factor model for the SHAPE scale, comprising two
inter-correlated subscales. | Society’sAttitudesTowardsHumanAugmentation |
Philip Feldman, James R. Foulds, and Shimei Pan. 2023.
Trapping llm hallucinations using tagged context
prompts.
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony
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Vinija Jain. 2023. Hallucination mitigation. Distilled
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Gemini: A Family of Highly Capable Multimodal Models
Contributors
Geoffrey Irving
Edward Loper
Manaal Faruqui
Isha Arkatkar
Nanxin Chen
Izhak Shafran
Rama Pasumarthi
Nathan Lintz
Anitha Vijayakumar
Lam Nguyen Thiet
Pedro Valenzuela
Cosmin Paduraru
Daiyi Peng
Katherine Lee
Shuyuan Zhang
Somer Greene
Duc Dung Nguyen
Paula Kurylowicz
Sarmishta Velury
Sebastian Krause
Cassidy Hardin
Lucas Dixon
Lili Janzer
Kiam Choo
Ziqiang Feng
Biao Zhang
Achintya Singhal
Tejasi Latkar
Mingyang Zhang
Quoc Le
Elena Allica Abellan
Dayou Du
Dan McKinnon
Natasha Antropova
Tolga Bolukbasi
Orgad Keller
David Reid
Daniel Finchelstein
Maria Abi Raad
Remi Crocker
Peter Hawkins
Robert Dadashi
Colin Gaffney
Sid Lall
Ken Franko
Egor Filonov
Anna Bulanova
Rémi Leblond
39 | gemini_1_report |
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Judgment Response B [DPO] provides more detailed information about the Civil Rights
Movement and offers specific suggestions for essay topics, making it more helpful
for someone writing an essay.
Table 7: GPT-4 chooses DPO over GT. Sample responses to a prompt from the Anthropic-HH test set. DPO
sample generated with temperature 0.7; GT is the chosen completion in the dataset of preferences. For clarity,
post-hoc annotations are included in bold, formatted as [annotation]. These annotations are not part of the
model generations.
Prompt
DPO
GT | Direct Preference Optimization |
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surprising comedic effects, as the examples are shown in
Fig. 3.
It is worth noting that the character “頓” in both
Japanese and Chinese denote “sudden”, while “智” means
“intelligence, insight or intuition”. This highlights the con-
nection between the Oogiri game and the requirement for
strong associative abilities in LoT, making Oogiri an ideal
platform for exploring LoT capabilities within LLMs.
(2) Multimodal LLMs and their creativity. Recently,
multimodal Language Models [1, 29, 34, 35] have garnered
significant attention, particularly due to their impressive
reasoning abilities [7–12, 36]. Moreover, there is a growing
focus on exploring the creativity [37–40] of LLMs for ap-
plications such as scientific discovery [18, 41–44], creative
writing [45–49], etc.
(3) Computational humor is a branch of computational
linguistics and artificial intelligence that uses computers in
humor research [50], which encompasses various tasks, in- | Let’sThinkOutsidetheBox |
is a scary technology that could be a problem for our democracy. We
will not be able to distinguish real/fake or true/untrue. (N584) | Adoptionand AppropriationofLLMs |
mance downstream to a large degree. Whether the noisiness
of the progression reflects actual changes in the language
model’s bias or poor reliability of CrowS-Pairs is an open
question we leave for future work.
We propose that performing such modifications to portions
of language model training data, retraining, and comparing
to the baseline model (“interventions”) should be studied
further for applications including but not limited to investi-
gating bias amplification and devising new mitigation strate-
gies. For example, while not explored in this case study, we
think that the finegrained information that Pythia provides
on the data seen during training could benefit the promis-
ing literature on influence functions to estimate the role of
specific training samples on the encoded bias (Brunet et al.,
2019; Silva et al., 2022). While this was beyond the scope
of this case study, we believe that the extensive availability
of checkpoints, consistent training order, and retrainabil- | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
The latency improvement obtained using FA is significant for both Whisper and Distil-Whisper. At
batch size 1, distil-large-v2 is comparable to base.en, while distil-medium.en is faster than tiny.en.
However, the memory savings are not enough to offset the effects of the T4 GPU at higher batch
sizes; distil-large-v2 is slower than small.en at batch size 4 and 16, and distil-medium.en slower than
base.en.
Overall, a T4 GPU may be adequate for operating Whisper and Distil-Whisper models at a batch
size of 1. For batch sizes beyond this, there is a notable performance stagnation on a T4, and higher
memory A100 GPUs are preferential. | DISTIL-WHISPER |
About the Project
Applications are invited for a fully funded PhD studentship in Computer Vision and
Machine Learning on the topic of Long-Term Video Understanding.
The successful applicant will work in a vibrant computer Machine Learning and
Computer Vision lab, with more than 9 PhD students and 3 postdoctoral
researchers working on closely related topics. For an insight into the supervisors’
current and previous works, refer to:
Prof Dima Damen http://dimadamen.github.io/
Further Particulars
Candidate Requirements
Applicants must hold/achieve a minimum of a Master’s degree (or international
equivalent) in computer science, mathematics or other relevant
discipline. Applicants without a Master’s qualification may be considered on an
exceptional basis, provided they hold a first-class undergraduate degree. Please
note, acceptance will also depend on evidence of readiness to pursue a research
degree.
Basic skills and knowledge required:
· Essential: | Machine Learning for Long-Term Video Understanding at University of Bristol on FindAPhD.com |
//unesdoc.unesco.org/ark:/48223/pf0000385146.locale=en
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59, 3 (July 2021), 1234–1251. https://doi.org/10.1111/ecin.12978 | Adoptionand AppropriationofLLMs |
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A., Chen, A., Madaan, D., Nangia, N., Pang, R. Y., Phang,
J., et al. What do NLP researchers believe? Results of the
NLP community metasurvey. arXiv preprint 2208.12852,
2022.
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim,
C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al.
WebGPT: Browser-assisted question-answering with hu-
man feedback. arXiv preprint 2112.09332, 2021.
Ngo, R. The alignment problem from a deep learning per-
spective. arXiv preprint 2209.00626, 2022.
Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H.,
Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma,
M., Luan, D., et al. Show your work: Scratchpads for
intermediate computation with language models. arXiv
preprint 2112.00114, 2021.
Oliver, J. Last week tonight with John Oliver: Feb 26, 2023.
URL https://www.hbo.com/last-week-to
night-with-john-oliver/season-10/2-f
ebruary-26-2022. | Eight Things to Know about Large Language Models |
give logit output values and emphasizes that this
information is a supplementary source rather than
a necessary prerequisite for the hallucination
detection approach. The method uses retrieved
knowledge as support for the correction phase,
instructing the model to repair the phrase by
either eliminating or substituting hallucinated
information to reduce hallucinations in the created
sentence.
Decompose and Query framework (D&Q):
In their research,
(Cao et al.,
2023) address challenges faced by LLMs in
Question Answering, focusing on hallucinations
and difficulties with multi-hop relations. They
propose the D&Q framework to guide models in
utilizing external knowledge while constraining
reasoning to reliable information, thus mitigating
the risk of hallucinations. Experimental results
demonstrate D&Q’s effectiveness, showcasing
competitive performance against GPT-3.5 on
ChitChatQA and achieving a noteworthy 59.6%
F1 score on HotPotQA (question-only). The | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
5. Mixed Retrieval: The advantage of this strategy
lies in leveraging the strengths of different retrieval
technologies. Intelligently combining various tech-
niques, including keyword-based search, semantic
search, and vector search, adapts to different query
types and information needs, ensuring consistent
retrieval of the most relevant and context-rich in-
formation. Mixed retrieval can serve as a robust
complement to retrieval strategies, enhancing the
overall performance of the RAG pipeline.
Embedding
• Fine-turning Embedding: | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
4.2 Design and Analysis
Baselines. To comprehensively evaluate our mul-
timodal agent framework, we considered various
design choices and their impact on performance.
We conducted experiments using different configu-
rations to provide valuable insights into the agent’s
behavior. We started with GPT-4 without any ref-
erence documents during testing and examined its
performance both with the raw action API and
our simplified action space. Next, we explored
different ways to generate guiding documents for
the agent. These included documents generated
through autonomous exploration, watching human
demonstrations, and the manually crafted docu-
ment as an oracle benchmark.
To effectively compare the performance of dif-
6 | AppAgents |
hyponym-hypernym prediction, word-supersense
prediction, replaced entity detection, predication
prediction, dependency relation prediction, entity
linking).3 Our focus is on adding knowledge
about entities, so our work is closer to Zhang et al.
(2019); Peters et al. (2019); Xiong et al. (2019b);
Wang et al. (2020); Poerner et al. (2019) than to
the linguistically-augmented approaches of Levine
et al. (2019); Lauscher et al. (2019). Closest to
our work, KNOWBERT (Peters et al., 2019) intro-
duce an entity memory layer that is similar to the
one in EAE. In contrast with our work, KNOW-
BERT starts from the BERT checkpoint, does not
train with a knowledge-focused objective such as
our mention-masking input function and uses pre-
computed entity representations when integrating
the information from knowledge bases. In addi-
tion, KNOWBERT relies on a fixed, pre-existing
candidate detector (alias table) to identify potential
candidates and entities for a span while our model | Entities as Experts- Sparse Memory Access with Entity Supervision |
Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, and Dan Klein. Detoxifying
language models risks marginalizing minority voices, 2021. URL https://arxiv.org/abs/2104.06390.
Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, and
Colin Raffel. ByT5: Towards a token-free future with pre-trained byte-to-byte models. TACL, 2022. URL
https://aclanthology.org/2022.tacl-1.17.
Michihiro Yasunaga and Percy Liang. Graph-based, self-supervised program repair from diagnostic feedback.
In ICML, 2020. URL http://go/arxiv/2005.10636.
Qinyuan Ye, Bill Yuchen Lin, and Xiang Ren. Crossfit: A few-shot learning challenge for cross-task general-
ization in NLP. In EMNLP, 2021. URL https://arxiv.org/abs/2104.08835.
Ann Yuan, Daphne Ippolito, Vitaly Nikolaev, Chris Callison-Burch, Andy Coenen, and Sebastian Gehrmann.
Synthbio: A case study in human-ai collaborative curation of text datasets, 2021. URL https://arxiv.
org/abs/2111.06467. | Scaling Instruction-Finetuned Language Models |
non-matching references. Advances in Neural Information Processing Systems 34 (2021), 22363–22378.
[370] Narla John Metilda Sagaya Mary, Srinivasan Umesh, and Sandesh Varadaraju Katta. 2021. S-vectors and TESA:
Speaker embeddings and a speaker authenticator based on transformer encoder. IEEE/ACM Transactions on Audio,
Speech, and Language Processing 30 (2021), 404–413.
[371] Mitchell McLaren, Luciana Ferrer, Diego Castan, and Aaron Lawson. 2016. The speakers in the wild (SITW) speaker
recognition database.. In Interspeech. 818–822.
[372] Ivan Medennikov, Maxim Korenevsky, Tatiana Prisyach, Yuri Khokhlov, Mariya Korenevskaya, Ivan Sorokin, Tatiana
Timofeeva, Anton Mitrofanov, Andrei Andrusenko, Ivan Podluzhny, et al. 2020. Target-speaker voice activity
detection: a novel approach for multi-speaker diarization in a dinner party scenario. arXiv preprint arXiv:2005.07272
(2020). | AReviewofDeepLearningTechniquesforSpeechProcessing |
5 Pushing the Chatbot State-of-the-art with QLoRA
Having established that 4-bit QLORA matches 16-bit performance across scales, tasks, and datasets
we conduct an in-depth study of instruction finetuning up to the largest open-source language models
available for research. To assess the performance of instruction finetuning these models, we evaluate
7
Table 4: Mean 5-shot MMLU test accuracy for LLaMA 7-65B models finetuned with adapters on Alpaca and
FLAN v2 for different data types. Overall, NF4 with double quantization (DQ) matches BFloat16 performance,
while FP4 is consistently one percentage point behind both.
LLaMA Size
Dataset
BFloat16
Float4
NFloat4 + DQ
7B
Mean 5-shot MMLU Accuracy
13B
33B
Alpaca
38.4
37.2
39.0
FLAN v2 Alpaca
47.2
47.3
47.5
45.6
44.0
44.5
FLAN v2 Alpaca
57.7
55.9
57.3
50.6
50.0
50.7
FLAN v2 Alpaca
61.8
61.3
61.8
60.5
58.5
59.2
65B
FLAN v2
62.5
63.3
63.9
Mean
53.0
52.2
53.1 | QLORA |
In addition to this suite of external evaluations, specialist internal teams conduct ongoing red
teaming of our models across areas such as the Gemini policies and security. These activities include
less structured processes involving sophisticated adversarial attacks to identify new vulnerabilities.
Discovery of potential weaknesses can then be used to mitigate risks and improve evaluation ap-
proaches internally. We are committed to ongoing model transparency and plan to share additional
results from across our evaluation suite over time.
6.4. Mitigations
Mitigations are developed in response to the outcomes of the assessment, policy, and evaluation
approaches described above. Evaluations and mitigations are used in an iterative way, with evaluations
being re-run following mitigation efforts. We discuss our efforts on mitigating model harms across
data, instruction-tuning, and factuality below. | gemini_1_report |
traditional
campaigns. Journalism and Mass Communication Quarterly, 90(1), 23–38.
Rosenberg, M. (2019). Ad tool Facebook built to fight disinformation doesn’t work as
advertised. New York Times, July 25. www.nytimes.com/2019/07/25/technology/
facebook-ad-library.html
Shaw, D. R., Blunt, C., & Seaborn, B. (2018). Testing overall and synergistic campaign
effects in a partisan statewide election. Political Research Quarterly, 71(2),
361–379.
Singer, N. (2018a). Taking a spin through data behind ads for candidates. New York
Times, September 3. www.nytimes.com/2018/09/02/technology/03adarchive.html
Singer, N. (2018b). “Weaponized ad technology”: Facebook’s moneymaker gets
a critical eye. New York Times, August 16. www.nytimes.com/2018/08/16/
technology/facebook-microtargeting-advertising.html
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
138
Erika Franklin Fowler, Michael M. Franz, & Travis N. Ridout | Social_Media_and_Democracy |
Prompt Tuning. Prompt tuning is a technique used to enhance the performance of LLMs in supervised downstream tasks. It
formulates the downstream task into a masked language problem and converts the original token input into a template and
masking certain tokens unfilled for the LLMs to complete. By modifying the tunable template embedding, prompt tuning
aims to improving performance in the downstream tasks via reducing the distribution shift between the pretrained tasks
and the specified downstream tasks. This method also enables the LLM to engage in few-shot or even zero-shot learning,
especially useful in scenarios with limited supervised data, by generating new prompt templates. | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
for a given predicate. To cope with the computational costs of reasoning, the authors use an ad-hoc taxonomy of is-a,
has-a relationships. | Knowledge graphs as tools for explainable machine learning: A survey |
D.2
Instructions and Interface
We display basic task instructions in a pop-up dialog when first loading the interface, and these instructions
remain available throughout the interaction. These instructions for the ‘playground’ and ‘red team’ tasks can
be found in figure 41. For the playground task, we also link to a separate page with expanded instructions
that include more detailed examples, excerpts of which can be seen in figure 42.
The human feedback interface is shown in figure 6. During the online data collection process, we added
an additional option to the interface for Upworkers. This feature allowed them to edit one of the model
responses. When they used this feature, we stored a comparison of the edit to the original (assuming the edit
was better), rather than the initial comparison of two model outputs. This would have effected less than 10%
of the online data.
D.3 Data Quality Measurement Challenges | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Motivation and Background. Although LLM-based agents possess commendable text under-
standing and generation capabilities, they operate as isolated entities in nature [409]. They lack the
ability to collaborate with other agents and acquire knowledge from social interactions. This inherent
limitation restricts their potential to learn from multi-turn feedback from others to enhance their
performance [27]. Moreover, they cannot be effectively deployed in complex scenarios requiring
collaboration and information sharing among multiple agents.
As early as 1986, Marvin Minsky made a forward-looking prediction. In his book The Society of
Mind [442], he introduced a novel theory of intelligence, suggesting that intelligence emerges from
the interactions of many smaller agents with specific functions. For instance, certain agents might be
responsible for pattern recognition, while others might handle decision-making or generate solutions. | TheRiseandPotentialofLargeLanguageModel BasedAgents |
being addressed after training by using various techniques to better “align” the LLM with human
values (Stiennon et al., 2020; Bai et al., 2022; Perez et al., 2022). Other legal and ethical concerns
already arise during the pre-training phase, specifically regarding the rights of content creators
whose public data is used to train the language model. This data is subject to copyright laws in
many jurisdictions, including the U.S. and E.U. It has been questioned whether machine learning
models trained on such data fall under exemptions such as the fair-use doctrine in the U.S. (Kuhn,
2022; Butterick, 2022; Rothchild & Rothchild, 2022). It is likely considered fair use when a model
generates novel content that is not in the training set, as it is a transformative use of the copyrighted
material (Lemley & Casey, 2020). However, if the model produces output similar to copyrighted
data, particularly in scenarios that affect the economic market of the content creators, fair use may | StarCoder_paper (1) |
Regarding associable discrimination, we aim to develop
fundamental LoT discrimination skills for LLM. Based on
the Oogiri-GO data, we design choice questions to enhance
LLM’s LoT discrimination ability, i.e., selection skill. Be-
sides, as 77.95% of the Oogiri-GO data have human pref-
erence annotations, i.e., the number of likes of several re-
sponses (see Sec. 3), we design ranking questions to im-
prove another discrimination skill, i,e., ranking ability. | Let’sThinkOutsidetheBox |
3
(a) predictor vs relu
(b) low rank predictor
Figure 3: (a) Preactivations of tokens in one sequence in OPT 6.7B. The blue graph shows preactivation of elements
that predictor detected positive while the green graph is for up projection. As it can be seen most of the False
Positives are close to 0 and False Negatives constitute a small portion of the elements. (b) A small low rank predictor
finds out which intermediate neurons are going to be activated instead of running heavy up projection.
in RAM. For the Feed-Forward Network (FFN)
portions, only the non-sparse segments are dynam-
ically loaded into DRAM as needed. Storing at-
tention weights, which constitute approximately
one-third of the model’s size, in memory, allows
for more efficient computation and quicker access,
thereby enhancing inference performance without
the need for full model loading. | LLM in a flash |
Sure enough, as the models get bigger and bigger, they begin to deliver human-level, and then superhuman results.
Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications.
4 of 8
23/06/2023, 17:44
Generative AI: A Creative New World | Sequoia Capital
https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/
Models
expect to see higher quality outputs, longer-form content, and better vertical-specific tuning. | Generative AI A Creative New World Sequoia Capital |
Amirata Ghorbani, Abubakar Abid, and James Zou.
2019. Interpretation of neural networks is fragile.
In Proceedings of the AAAI Conference on Artificial
Intelligence.
Braden Hancock, Paroma Varma, Stephanie Wang, Mar-
tin Bringmann, Percy Liang, and Christopher Ré.
2018. Training classifiers with natural language ex-
planations. In Proceedings of the 56th Annual Meet-
ing of the Association for Computational Linguistics
(Volume 1: Long Papers), pages 1884–1895, Mel-
bourne, Australia. Association for Computational
Linguistics.
Peter Hase and Mohit Bansal. 2020. Evaluating explain-
able AI: Which algorithmic explanations help users
predict model behavior? In Proceedings of the 58th
Annual Meeting of the Association for Computational
Linguistics, pages 5540–5552, Online. Association
for Computational Linguistics. | Measuring Association Between Labels and Free-Text Rationales |
7 UNDERSTANDING THE LOW-RANK UPDATES
Given the empirical advantage of LoRA, we hope to further explain the properties of the low-rank
adaptation learned from downstream tasks. Note that the low-rank structure not only lowers the
hardware barrier to entry which allows us to run multiple experiments in parallel, but also gives
better interpretability of how the update weights are correlated with the pre-trained weights. We
focus our study on GPT-3 175B, where we achieved the largest reduction of trainable parameters
(up to 10,000×) without adversely affecting task performances.
We perform a sequence of empirical studies to answer the following questions: 1) Given a parameter
budget constraint, which subset of weight matrices in a pre-trained Transformer should we adapt
9 | LORA |
the models are adapted to news one week/month before the time the survey was conducted. (C) Our hypothesis is that the target word
probabilities, which are updated after finetuning BERT, reflect media effects. These in turn are predictive of the response distributions found
in surveys. The media diet scores are used to predict the response proportions, combining data over multiple media diets and surveys. In
additional analyses, we include demographic stats and information about how closely respondents were paying attention to news. | Language models trained on media diets can predict public opinion |
Computers as cognitive tools, pp. 269–296. Routledge, 2013.
Guillaume Lample, Timothee Lacroix, Marie-Anne Lachaux, Aurelien Rodriguez, Amaury Hayat, Thibaut
Lavril, Gabriel Ebner, and Xavier Martinet. Hypertree proof search for neural theorem proving. Advances
in Neural Information Processing Systems, 35:26337–26349, 2022.
Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. Internet-augmented
language models through few-shot prompting for open-domain question answering. ArXiv preprint,
abs/2203.05115, 2022. URL https://arxiv.org/abs/2203.05115.
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen. Learning hand-eye coordina-
tion for robotic grasping with deep learning and large-scale data collection. The International journal of
robotics research, 37(4-5):421–436, 2018. | Tool Learning with Foundation Models |
[37] Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael
Bendersky, and Marc Najork. WIT: wikipedia-based image
text dataset for multimodal multilingual machine learning. In
SIGIR ’21: The 44th International ACM SIGIR Conference on
Research and Development in Information Retrieval, Virtual
Event, Canada, July 11-15, 2021, pages 2443–2449. ACM,
2021. 1, 5
[38] Hao Tan and Mohit Bansal. LXMERT: Learning cross-
In
modality encoder representations from transformers.
Proceedings of the 2019 Conference on Empirical Methods
in Natural Language Processing and the 9th International
Joint Conference on Natural Language Processing (EMNLP-
IJCNLP), pages 5100–5111, Hong Kong, China, 2019. Asso-
ciation for Computational Linguistics. 6 | REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory |
– Black Alternative Metal, The Pick of Death (Deluxe), 2006,
3 of 4
– Death Metal, 2012, 3 of 4
– Drops, Kanine Remix, Darkzy, Drops Remixes, bass house,
(Deluxe) (Remix), 3 of 4
– EDM (Deluxe) (Remix), 3 of 4
– Electro House (Remix), 2023, 3 of 4
– Electro Swing Remix 2030 (Deluxe Edition), 3 of 4
– Future Bass, EDM (Remix), Remix, 3 of 4
– Hip Hop Tech, Bandlez, Hot Pursuit, brostep, 3 of 4
– Italian Hip Hop 2022 (Deluxe Edition), 3 of 4
– Heavy metal (Deluxe Edition), 3 of 4
– The Heavy Death Metal War (Deluxe), 2006, 3 of 4
– Pop, Taylor Swift, Speak Now, 2014, (Deluxe), 3 of 4
– Melodic Metal, Iron Dust (Deluxe), 2006, 3 of 4
– Electronic, Dance, EDM (Deluxe) (Remix), 3 of 4
– Alternative Hip Hop Oh-My, 2016, (Deluxe), 3 of 4
– Viking Heavy Death Metal (Deluxe), 2006, 3 of 4
– Possessed Death Metal Stones (Deluxe), 2006, 3 of 4
– Hardstyle, Drop, 8D, Remix, High Quality, 2 of 4
– Drop, French 79, BPM Artist, Vol. 4, Electronica, 2016 | Moûsai |
When using large guidance weights, the resulting ˜xθ(zt, c) must be projected back to the pos-
sible range of pixel values at every sampling step to prevent train-test mismatch. When using
large guidance weights, the standard approach, i.e., clipping the values to the right range (e.g.,
np.clip(x, -1, 1)), leads to significant saturation artifacts in the generated videos. A sim-
ilar effect was observed in Saharia et al. (2022b) for text-to-image generation. Saharia et al.
(2022b) use dynamic thresholding to alleviate this saturation issue. Specifically, dynamic clipping
involves clipping the image to a dynamically chosen threshold s followed by scaling by s (i.e.,
np.clip(x, -s, s) / s) (Saharia et al., 2022b).
Although dynamic clipping can help with over-saturation, we did not find it sufficient in initial ex-
periments. We therefore also experiment with letting w oscillate between a high and a low guidance | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
3.3. Seeing the whole elephant, a little bit at a time
The good news is that if we can start to work together, progress may not be so far away.
If the problem of robust intelligence had already been solved, there would be no need to
19 A second cultural issue, as one reader of this manuscript pointed out, is that advocates of deep learning
have often put far too much stock in big data, often assuming, sometimes incorrectly, that the answers
to complex problems can largely be found in ever-bigger data sets and larger and larger clusters of
compute. Whole fields, such as linguistics, have largely been dismissed along the way. This cannot be
good.
20 Strictly speaking, Planck never actually said quite that: see
https://quoteinvestigator.com/2017/09/25/progress/
52
THE NEXT DECADE IN AI / GARY MARCUS | The Next Decade in AI- |
University Preparatory Certificate
2.7.1 University Preparatory Certificate for Science & Engineering and University
Preparatory Certificate for Humanities
1.
International applicants whose secondary education qualifications are not suitable for direct
admission to leading UK universities may apply for a one-year programme for Science and
Engineering or Humanities offered by UCL.
2. Successful completion of the one-year programme may be used to apply for an undergraduate
programme of study at UCL or other university.
3. Entrance requirements by country can be obtained from the Centre for Languages and
4. All applicants are required to take an entrance test and further information can be obtained from
International Education (CLIE).
the (CLIE). | UCL Academic Manual |
A study by Long [150] proposed attention-based LSTM
with speaker profile features, and their experimental findings
suggest that employing speaker profiles can help enhance
fake news identification. Recently, attention techniques have
been used to efficiently extract information related to a mini
query (article headline) from a long text (news content) [47],
[87]. A study by Singhania et al. [87] used an automated
detector through a three-level hierarchical attention network
(3HAN). Three levels exist in 3HAN, one for words, one
for sentences, and one for the headline. Because of its three
levels of attention, 3HAN assigns different weights to differ-
ent sections of an article. In contrast to other deep learning
models, 3HAN yields understandable results. While 3HAN
only uses textual information, a study by Jin et al. [47] used
image features, including social context and text features, as
well as attention on RNN (att-RNN). Another study used | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch,
Michael Rubinstein, and Kfir Aberman. 2022. Dream-
booth: Fine tuning text-to-image diffusion models for
subject-driven generation. ArXiv, abs/2208.12242.
Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang,
Chao Weng, Yuexian Zou, and Dong Yu. 2022. Diff-
sound: Discrete diffusion model for text-to-sound gen-
eration. CoRR, abs/2207.09983.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala
Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed
Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi,
Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho,
Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei
Ye, Shikun Zhang, Tao Qin, and Tie-Yan Liu. 2022a.
Museformer: Transformer with fine- and coarse-grained
attention for music generation. CoRR, abs/2210.10349. | MOUSAI |
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