Efficient Instruction Fine-Tuning of Large Language Models through Shapley-Based Automated Dataset Refinement

Core Concepts

SHED, an automated dataset refinement framework based on Shapley value, can curate small yet high-quality datasets to efficiently fine-tune large language models for instruction-following tasks.

Abstract

The paper introduces SHED, a Shapley value-based framework for refining datasets to enable efficient fine-tuning of large language models (LLMs) for instruction-following tasks.
Key highlights:

SHED consists of three components: model-agnostic clustering, proxy-based Shapley calculator, and optimization-aware sampling.
The model-agnostic clustering groups the dataset into clusters and selects representative proxy data for each cluster.
The proxy-based Shapley calculator efficiently estimates the Shapley values of the proxy data by iteratively removing groups of instances and evaluating the performance impact.
The optimization-aware sampling uses the Shapley values as quality scores to select a small yet high-quality dataset for fine-tuning.
Extensive experiments on MMLU and WizardLLM datasets show that LLMs fine-tuned on datasets curated by SHED outperform or match the performance of models fine-tuned on the full original datasets, while using only 10% of the data.
The datasets curated by SHED exhibit strong transferability, maintaining robust performance across different LLM models.
SHED provides a flexible framework that can be customized to optimize for various objectives, such as model fairness, beyond just accuracy.

Stats

LLMs can achieve desirable performance with only a small amount of high-quality data, suggesting that a large amount of the data in extensive datasets is redundant or even harmful.
Fine-tuning LLMs on extensive datasets incurs significant computational costs, limiting broader applications.
Directly computing Shapley values for all data samples in a dataset is computationally expensive, especially for large-scale fine-tuning datasets.

Quotes

"Recent studies have discovered that LLMs can achieve desirable performance with only a small amount of high-quality data, suggesting that a large amount of the data in these extensive datasets is redundant or even harmful."
"Fine-tuning LLMs on extensive datasets incurs significant computational costs, presenting a critical challenge. Only those researchers and institutions equipped with sufficient computing resources are able to perform such tasks, limiting the broader applications and progress within the LLM community."
"Directly computing Shapley values for all data samples in a dataset is computationally expensive, especially for large-scale fine-tuning datasets."

Key Insights Distilled From

SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

by Yexiao He,Zi... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00705.pdf

Deeper Inquiries

How can SHED's framework be extended to optimize for other objectives beyond accuracy, such as model fairness and robustness?

SHED's framework can be extended to optimize for other objectives by customizing the value function used in the Shapley value calculations. For instance, to optimize for model fairness, the value function can be defined to measure disparities in model predictions across different demographic groups. By incorporating fairness metrics into the value function, SHED can select data points that promote fairness in model outcomes. Similarly, to enhance model robustness, the value function can be designed to evaluate the stability and generalization capabilities of the model. By incorporating robustness metrics, SHED can select data that improves the model's ability to generalize to unseen data and withstand adversarial attacks. Overall, by adapting the value function to reflect specific objectives such as fairness and robustness, SHED can curate datasets that optimize for these goals in addition to accuracy.

What are the potential limitations of the proxy-based Shapley value estimation approach, and how can it be further improved to enhance the accuracy of the Shapley value calculations?

One potential limitation of the proxy-based Shapley value estimation approach is the reliance on approximations, which may introduce errors in the Shapley value calculations. The iterative removal of groups of instances to estimate their collective contribution can lead to inaccuracies, especially when the proxy data does not fully represent the diversity and complexity of the original dataset. To enhance the accuracy of the Shapley value calculations, several improvements can be implemented.
Firstly, increasing the number of iterations in the proxy-based Shapley calculator can improve the accuracy of the estimates by refining the approximation over multiple iterations. Additionally, refining the clustering algorithm to create more representative clusters and proxy data can enhance the accuracy of the Shapley value calculations. Incorporating more sophisticated approximation methods or sampling techniques can also help reduce errors in the estimation process. By continuously refining and optimizing the proxy-based approach, the accuracy of the Shapley value calculations can be improved.

How can the transferability of the datasets curated by SHED be leveraged to amortize the computational cost of data selection across different LLM models and tasks?

The transferability of datasets curated by SHED can be leveraged to amortize the computational cost of data selection across different LLM models and tasks by creating a repository of high-quality, transferable datasets. These datasets, curated based on Shapley values and optimized for specific objectives, can be reused across various LLM models and tasks without the need for extensive data selection processes for each new model or task.
By leveraging the transferability of these datasets, researchers and practitioners can save time and computational resources by using pre-selected, high-quality datasets for fine-tuning different LLM models. This approach not only streamlines the data selection process but also ensures consistent performance across different models, as the curated datasets have demonstrated robustness and effectiveness across various tasks.
Furthermore, the curated datasets can be shared within the research community, enabling collaboration and knowledge sharing. By establishing a standardized set of transferable datasets curated by SHED, researchers can focus more on model development and experimentation, knowing that they have access to high-quality data that aligns with specific objectives. This approach ultimately leads to more efficient and cost-effective fine-tuning processes in the LLM domain.