Automatic Data Selection Strategies for Efficient Instruction Tuning of Large Language Models

Alapfogalmak

Effective data selection is critical for achieving superior performance in instruction tuning of large language models with limited training data.

Kivonat

This paper presents a comprehensive study on automatic data selection strategies for instruction tuning of large language models. The authors start with controlled experiments to measure data across three key dimensions: complexity, quality, and diversity. They then introduce novel techniques to enhance the measurement of these data characteristics.
Based on the data measurement, the authors propose a simple strategy to automatically select the most suitable data samples for instruction tuning. They introduce DEITA, a series of models fine-tuned from LLaMA and Mistral using the automatically selected data.
Empirically, the DEITA models achieve better or on-par performance compared to state-of-the-art open-source alignment models, while using only 6K SFT training samples - over 10x less than the baselines. When further trained with direct preference optimization (DPO), the DEITA-Mistral-7B + DPO model attains 7.55 MT-Bench and 90.06% AlpacaEval scores.
The authors anticipate this work to provide valuable tools for automatic data selection, enabling more efficient alignment of large language models. They release the DEITA model checkpoints and the selected datasets for future research.

Statisztikák

"almost all knowledge in LLMs is acquired during pretraining, and instruction tuning is to align the model to end tasks and user preferences"
"only limited data is necessary to achieve superior performance" in instruction tuning

Idézetek

"Differing from traditional task-specific fine-tuning where data quantity is paramount, past studies argue that almost all knowledge in LLMs is acquired during pretraining, and instruction tuning is to align the model to end tasks and user preferences."
"Recent research indicates the critical role of data engineering in instruction tuning – when appropriately selected, only limited data is necessary to achieve superior performance."

Főbb Kivonatok

What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

by Wei Liu,Weih... : arxiv.org 04-17-2024

https://arxiv.org/pdf/2312.15685.pdf

Mélyebb kérdések

What other data characteristics beyond complexity, quality, and diversity could be important for effective instruction tuning data selection

In addition to complexity, quality, and diversity, other data characteristics that could be crucial for effective instruction tuning data selection include relevance, representativeness, and bias. Relevance ensures that the selected data aligns closely with the specific task or user preferences being targeted during instruction tuning. Representativeness ensures that the selected data samples adequately cover the range of possible inputs and scenarios that the model may encounter in real-world applications. Bias considerations are essential to avoid reinforcing or amplifying any existing biases present in the data, which could lead to undesirable outcomes in the model's responses.

How generalizable are the proposed automatic data selection strategies across different large language models and instruction tuning tasks

The proposed automatic data selection strategies for instruction tuning can be highly generalizable across different large language models and instruction tuning tasks. The focus on data efficiency and the principled understanding of what makes good instruction tuning data for alignment can be applied to various models and tasks within the realm of natural language processing. By measuring data characteristics like complexity, quality, and diversity, and using novel techniques for enhanced data measurement, the strategies can be adapted and tailored to different models and tasks, providing a systematic approach to data selection for alignment.

What are the potential ethical considerations and risks in developing highly data-efficient instruction tuning techniques for large language models

Developing highly data-efficient instruction tuning techniques for large language models raises important ethical considerations and risks. One key ethical consideration is the potential reinforcement of biases present in the data used for instruction tuning. If the selected data samples are biased or contain sensitive information, the model's responses could perpetuate or amplify these biases, leading to harmful outcomes. Additionally, there is a risk of unintended consequences, where the model's alignment to specific tasks or user preferences may inadvertently result in undesirable or harmful outputs. Transparency in the data selection process and ongoing monitoring for ethical implications are essential to mitigate these risks and ensure responsible development and deployment of data-efficient instruction tuning techniques.