Answers.Body: <p>For your task involving small-sample fine-tuning of pretrained large models in a live production environment, Sage et al. (2021). <a href="https://dl.acm.org/doi/abs/10.1007/978-3-030-86159-9_33" rel="nofollow noreferrer"><em>&quot;Data-Efficient Information Extraction from Documents with Pre-trained Language Models&quot;</em></a> is relevant.</p>
  <blockquote>
  <p>In this paper, we show that LayoutLM, a pre-trained model recently proposed for encoding 2D documents, reveals a high sample-efficiency when fine-tuned on public and real-world Information Extraction (IE) datasets. Indeed, LayoutLM reaches more than 80% of its full performance with as few as 32 documents for fine-tuning. When compared with a strong baseline learning IE from scratch, the pre-trained model needs between 4 to 30 times fewer annotated documents in the toughest data conditions. Finally, LayoutLM performs better on the real-world dataset when having been beforehand fine-tuned on the full public dataset, thus indicating valuable knowledge transfer abilities.</p>
  </blockquote>
  <p>The paper's focus is on balancing data quality and noise levels as you rightly intuited. It is crucial to maintain a <em>high</em> ratio of quality data to noisy data to avoid overfitting and enhance generalization particularly when dealing with <em>small samples</em>.</p>
  <p>A more recent paper studied parameter-efficient fine-tuning methods providing various strategies to extract and utilize data effectively from large pretrained models which is Xu et al. (2023). <a href="https://arxiv.org/abs/2312.12148" rel="nofollow noreferrer"><em>&quot;Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment
  &quot;</em></a>.</p>
  <blockquote>
  <p>With the continuous growth in the number of parameters of transformer-based pretrained language models (PLMs)... the enormous size and computational demands of these models pose significant challenges for adapting them to specific downstream tasks, especially in environments with limited computational resources. Parameter Efficient Fine-Tuning (PEFT) offers an effective solution by reducing the number of fine-tuning parameters and memory usage while achieving comparable performance to full fine-tuning... By offering insights into the latest advancements and practical applications, this survey serves as an invaluable resource for researchers and practitioners seeking to navigate the challenges and opportunities presented by PEFT in the context of PLMs.</p>
  </blockquote>
  <p>Another relevant more technical 2023 paper is Liu et al. <a href="https://aclanthology.org/2023.emnlp-main.748/" rel="nofollow noreferrer"><em>&quot;PAC-tuning: Fine-tuning Pre-trained Language Models with PAC-driven Perturbed Gradient Descent&quot;</em></a>.</p>
  <blockquote>
  <p>Fine-tuning pretrained language models (PLMs) for downstream tasks is a large-scale optimization problem, in which the choice of the training algorithm critically determines how well the trained model can generalize to unseen test data, especially in the context of few-shot learning. To achieve good generalization performance and avoid overfitting, techniques such as data augmentation and pruning are often applied. However, adding these regularizations necessitates heavy tuning of the hyperparameters of optimization algorithms, such as the popular Adam optimizer... First, based on PAC-Bayes training, PAC-tuning directly minimizes the PAC-Bayes generalization bound to learn proper parameter distribution. Second, PAC-tuning modifies the gradient by injecting noise with the variance learned in the first stage into the model parameters during training, resulting in a variant of perturbed gradient descent (PGD)... Our experimental results across 5 GLUE benchmark tasks demonstrate that PAC-tuning successfully handles the challenges of fine-tuning tasks and outperforms strong baseline methods by a visible margin</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-08-08T07:53:25.163Z
Answers.LastActivityDate: 2024-08-08T07:53:25.163Z
Answers.UserDisplayName: cinch
Body: <p>I'm currently working on a production task to perform small-shot fine-tuning of large pretrained models in a live production environment. Given that we plan to fine-tune directly from the data generated by the model, rather than using traditional external datasets, I urgently need to find relevant and up-to-date research papers and materials to guide our work.</p>
  <p>The research needs are as follows:<br />
  1.Data extraction strategy: We are looking for ways to extract data from large models for fine-tuning. If you know about relevant research work or have a recommended paper, please share generously.<br />
  2.Small-sample fine-tuning practices: We are exploring the best practices for small-sample fine-tuning. Especially when it comes to fine-tuning with small data sets, I seek specific requirements and recommendations for data quality. For example, what is the ideal ratio between noisy and high-quality data in a dataset? This will help us fine-tune the model more effectively, avoid overfitting, and improve the generalization ability of the model in practical applications. While data augmentation can be useful in some cases, for the time being we would like to leave this path aside in order to focus on other aspects of data selection and processing.</p>
  <p>Help:<br />
  1.Recommend research directions or papers that meet the above criteria.<br />
  2.If possible, please provide a link to the paper or DOI</p>
  <p>*Thesis Requirements:<br />
  1.Publication date: Preferably 2023 and onwards.<br />
  2.Closely related to the actual production environment.<br />
  3.If possible, please provide a link to the paper, a DOI, or other means of access.</p>
  <p>We take this project very seriously and believe that any form of help will greatly advance our work. We look forward to hearing from you and suggestions!</p>
  <p>Thank you from the bottom of our hearts for your time and support!</p>
  
Tags: <datasets><fine-tuning>
Title: A Request for Research on Small-Sample Fine-Tuning of Large Models in Actual Production
UserDisplayName: user26653270
ThreadText: # A Request for Research on Small-Sample Fine-Tuning of Large Models in Actual Production
  
  (Asked by: username_0 on 2024-08-08)
  
  I'm currently working on a production task to perform small-shot fine-tuning of large pretrained models in a live production environment. Given that we plan to fine-tune directly from the data generated by the model, rather than using traditional external datasets, I urgently need to find relevant and up-to-date research papers and materials to guide our work.
  
  The research needs are as follows:  
  1.Data extraction strategy: We are looking for ways to extract data from large models for fine-tuning. If you know about relevant research work or have a recommended paper, please share generously.  
  2.Small-sample fine-tuning practices: We are exploring the best practices for small-sample fine-tuning. Especially when it comes to fine-tuning with small data sets, I seek specific requirements and recommendations for data quality. For example, what is the ideal ratio between noisy and high-quality data in a dataset? This will help us fine-tune the model more effectively, avoid overfitting, and improve the generalization ability of the model in practical applications. While data augmentation can be useful in some cases, for the time being we would like to leave this path aside in order to focus on other aspects of data selection and processing.
  
  Help:  
  1.Recommend research directions or papers that meet the above criteria.  
  2.If possible, please provide a link to the paper or DOI
  
  \*Thesis Requirements:  
  1.Publication date: Preferably 2023 and onwards.  
  2.Closely related to the actual production environment.  
  3.If possible, please provide a link to the paper, a DOI, or other means of access.
  
  We take this project very seriously and believe that any form of help will greatly advance our work. We look forward to hearing from you and suggestions!
  
  Thank you from the bottom of our hearts for your time and support!
  
  **Tags:** datasets, fine-tuning
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-08-08. Score: 0
  
  For your task involving small-sample fine-tuning of pretrained large models in a live production environment, Sage et al. (2021). [*"Data-Efficient Information Extraction from Documents with Pre-trained Language Models"*](https://dl.acm.org/doi/abs/10.1007/978-3-030-86159-9_33) is relevant.
  
  > In this paper, we show that LayoutLM, a pre-trained model recently proposed for encoding 2D documents, reveals a high sample-efficiency when fine-tuned on public and real-world Information Extraction (IE) datasets. Indeed, LayoutLM reaches more than 80% of its full performance with as few as 32 documents for fine-tuning. When compared with a strong baseline learning IE from scratch, the pre-trained model needs between 4 to 30 times fewer annotated documents in the toughest data conditions. Finally, LayoutLM performs better on the real-world dataset when having been beforehand fine-tuned on the full public dataset, thus indicating valuable knowledge transfer abilities.
  
  The paper's focus is on balancing data quality and noise levels as you rightly intuited. It is crucial to maintain a *high* ratio of quality data to noisy data to avoid overfitting and enhance generalization particularly when dealing with *small samples*.
  
  A more recent paper studied parameter-efficient fine-tuning methods providing various strategies to extract and utilize data effectively from large pretrained models which is Xu et al. (2023). [*"Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment
  "*](https://arxiv.org/abs/2312.12148).
  
  > With the continuous growth in the number of parameters of transformer-based pretrained language models (PLMs)... the enormous size and computational demands of these models pose significant challenges for adapting them to specific downstream tasks, especially in environments with limited computational resources. Parameter Efficient Fine-Tuning (PEFT) offers an effective solution by reducing the number of fine-tuning parameters and memory usage while achieving comparable performance to full fine-tuning... By offering insights into the latest advancements and practical applications, this survey serves as an invaluable resource for researchers and practitioners seeking to navigate the challenges and opportunities presented by PEFT in the context of PLMs.
  
  Another relevant more technical 2023 paper is Liu et al. [*"PAC-tuning: Fine-tuning Pre-trained Language Models with PAC-driven Perturbed Gradient Descent"*](https://aclanthology.org/2023.emnlp-main.748/).
  
  > Fine-tuning pretrained language models (PLMs) for downstream tasks is a large-scale optimization problem, in which the choice of the training algorithm critically determines how well the trained model can generalize to unseen test data, especially in the context of few-shot learning. To achieve good generalization performance and avoid overfitting, techniques such as data augmentation and pruning are often applied. However, adding these regularizations necessitates heavy tuning of the hyperparameters of optimization algorithms, such as the popular Adam optimizer... First, based on PAC-Bayes training, PAC-tuning directly minimizes the PAC-Bayes generalization bound to learn proper parameter distribution. Second, PAC-tuning modifies the gradient by injecting noise with the variance learned in the first stage into the model parameters during training, resulting in a variant of perturbed gradient descent (PGD)... Our experimental results across 5 GLUE benchmark tasks demonstrate that PAC-tuning successfully handles the challenges of fine-tuning tasks and outperforms strong baseline methods by a visible margin
  
  
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