 song for the immortal and immaculate Serena Williams, but her impact and inspiration for future athletes will only continue to grow.
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The 2022 Collegian fantasy football guide
OT: Albert Pujols’ chase to 700 career home runsAt Vanishing Inc. we are always happy to help answer your questions about Amazing Magic Tricks with Money and aim to reply within about six hours.
To find out how much it will cost to ship Amazing Magic Tricks with Money to your country, please add the product to your cart. You will then see a shipping quote on the checkout page.Detailed Instructions: This task is about identifying the object of a given sentence. The object of a sentence is the person or thing that receives the action of the verb. It is the who or what that the subject does something to. Each sentence in this task is guaranteed to contain a single object. The input is a sentence and the output is the identified object of that sentence.
Problem:Maxime Dillies plays as a setter, or palleggiatore (P).
Solution:
PThe detailed plan to reopen Moor Hall offers insight into how restaurants will work for the foreseeable future in the UK and beyond.
Agreed, but does depend if the OP referenced the phrasing or not. Either way does not mean that the tutor can automatically give zero for the assignment based on the similarity report. I've seen phrasing properly referenced still give a high similarity report. 

The university has a policy on how academic misconduct should be treated and it was not followed in this case. There is also a range of penalties that can apply, maybe zero for the assignment is the appropriate penalty, maybe it's not. That is not up to the marker to decide, rather it's up to any misconduct panel to decide.ChatGPT is the latest language model from OpenAI and represents a significant improvement over its predecessor GPT-3. Similarly to many Large Language Models, ChatGPT is capable of generating text in a wide range of styles and for different purposes, but with remarkably greater precision, detail, and coherence. It represents the next generation in OpenAI's line of Large Language Models, and it is designed with a strong focus on interactive conversations.
The creators have used a combination of both Supervised Learning and Reinforcement Learning to fine-tune ChatGPT, but it is the Reinforcement Learning component specifically that makes ChatGPT unique. The creators use a particular technique called Reinforcement Learning from Human Feedback (RLHF), which uses human feedback in the training loop to minimize harmful, untruthful, and/or biased outputs.
We are going to examine GPT-3's limitations and how they stem from its training process, before learning how RLHF works and understand how ChatGPT uses RLHF to overcome these issues. We will conclude by looking at some of the limitations of this methodology.
"alignment vs capability" can be thought of as a more abstract analogue of "accuracy vs precision"
In the context of machine learning, the term capability refers to a model's ability to perform a specific task or set of tasks. A model's capability is typically evaluated by how well it is able to optimize its objective function, the mathematical expression that defines the goal of the model. For example, a model designed to predict stock market prices might have an objective function that measures the accuracy of the model's predictions. If the model is able to accurately predict the movement of stock prices over time, it would be considered to have a high level of capability for this task.
Alignment, on the other hand, is concerned with what we actually want the model to do versus what it is being trained to do. It asks the question “is that objective function consistent with our intentions?” and refers to the extent to which a model's goals and behavior align with human values and expectations. For a simple concrete example, say we train a bird classifier to classify birds as either "sparrows" or "robins" and we use log loss (which measures the difference between the predicted probability distribution of the model and the true distribution) as the training objective, even though our ultimate goal is a high classification accuracy. The model might have low log loss, i.e. the model’s capability is high, but poor accuracy on the test set. In fact, the log loss is not perfectly correlated with accuracy in classification tasks. This is an example of misalignment, where the model is capable of optimizing the training objective but poorly aligned with our ultimate goal.
Large Language Models, such as GPT-3, are trained on vast amounts of text data from the internet and are capable of generating human-like text, but they may not always produce output that is consistent with human expectations or desirable values. In fact, their objective function is a probability distribution over word sequences (or token sequences) that allows them to predict what the next word is in a sequence (more details on this below).
In practical applications, however, these models are intended to perform some form of valuable cognitive work, and there is a clear divergence between the way these models are trained and the way we would like to use them. Even though a machine calculated statistical distribution of word sequences might be, mathematically speaking, a very effective choice to model language, we as humans generate language by choosing text sequences that are best for the given situation, using our background knowledge and common sense to guide this process. This can be a problem when language models are used in applications that require a high degree of trust or reliability, such as dialogue systems or intelligent personal assistants.
Lack of helpfulness: not following the user's explicit instructions.
Hallucinations: model making up unexisting or wrong facts.
Lack of interpretability: it is difficult for humans to understand how the model arrived at a particular decision or prediction.
Generating biased or toxic output: a language model that is trained on biased/toxic data may reproduce that in its output, even if it was not explicitly instructed to do so.
But where does this alignment problem stem from, concretely? Is it the very way language models are trained inherently prone to misalignment?
"The cat sat on the"
it might predict the next word as "mat", "chair", or "floor", because of the high-probability of occurrence of these words given the previous context; the language model is in fact able to estimate the likelihood of each possible word (in its vocabulary) given the previous sequence.
"The [MASK] sat on the"
as input, it might predict the next word as "cat", "dog", or "rabbit".
One advantage of these objective functions is that it allows the model to learn the statistical structure of language, such as common word sequences and patterns of word usage. This generally helps the model generate more natural and fluent text, and it is an essential step in the pre-training phase of every language model.
"The Roman Empire [MASK] with the reign of Augustus."
it might predict "began" or “ended”, as both words score high likelihood of occurrence (indeed, both sentences are historically correct), even though the second choice implies a very different meaning.
More generally, these training strategies can lead to a misalignment of the language model for some more complex tasks, because a model which is only trained to predict the next word (or a masked word) in a text sequence, may not necessarily be learning some higher-level representations of its meaning. As a result, the model struggles to generalize to tasks or contexts that require a deeper understanding of language.
Researchers and developers are working on various approaches to address the alignment problem in Large Language Models. ChatGPT is based on the original GPT-3 model, but has been further trained by using human feedback to guide the learning process with the specific goal of mitigating the model’s misalignment issues. The specific technique used, called Reinforcement Learning from Human Feedback, is based on previous academic research. ChatGPT represents the first case of use of this technique for a model put into production.
But how exactly do the creators of ChatGPT use human feedback to attack the alignment problem?
Supervised fine-tuning step: a pre-trained language model is fine-tuned on a relatively small amount of demonstration data curated by labelers, to learn a supervised policy (the SFT model) that generates outputs from a selected list of prompts. This represents the baseline model.
“Mimic human preferences” step: labelers are asked to vote on a relatively large number of the SFT model outputs, this way creating a new dataset consisting of comparison data. A new model is trained on this dataset. This is referred to as the reward model (RM).
Proximal Policy Optimization (PPO) step: the reward model is used to further fine-tune and improve the SFT model. The outcome of this step is the so-called policy model.
Step 1 takes place only once, while steps 2 and 3 can be iterated continuously: more comparison data is collected on the current best policy model, which is used to train a new reward model and then a new policy.
Let’s now dive into the details of each step!
Note: The rest of this article is based on the content of the InstructGPT paper. According to OpenAI, ChatGPT has been trained “using the same methods as InstructGPT, but with slight differences in the data collection setup” (source). Unfortunately, exact quantitative reports have yet to be made publicly available for ChatGPT.
The first step consists in collecting demonstration data in order to train a supervised policy model, referred to as the SFT model.
Data collection: a list of prompts is selected and a group of human labelers are asked to write down the