Key Points

1. The paper introduces a method named R E A LIGN that aims to elevate the quality of existing instruction data to better align with human values. This approach minimizes human annotation, hallucination, and difficulty in scaling, significantly boosting the general alignment ability, math reasoning, factuality, and readability of large language models (LLMs).

2. The R E A LIGN method demonstrates a significant improvement in LLaMA-2-13B’s mathematical reasoning ability on GSM8K without introducing additional data or training techniques, and even a mere 5% of R E A LIGN data leads to a 67% boost in general alignment ability based on the Alpaca dataset.

3. Previous methods to improve data quality involved labor-intensive manual creation of high-quality data or automated extraction of high-quality instructions from existing datasets, each with their limitations. R E A LIGN, however, focuses on reformatting existing instruction data into a format that better aligns with pre-established criteria and collated evidence.

4. The proposed method leverages the complementary strengths of humans and LLMs in the alignment process, where humans define their preferences, and LLMs reconstruct instructions based on their generative power, minimizing factual errors and reducing the need for extensive human annotation.

5. R E A LIGN has been operationalized on various existing instruction data and validated across well-established benchmarks, demonstrating its proficiency in boosting math reasoning, general alignment ability, factuality, and readability.

6. The paper evaluates R E A LIGN on general alignment and specific alignment abilities, showing significant improvement in math reasoning, factuality, and readability on various datasets and benchmarks.

7. The study also explores the impact of the number of R E A LIGN data, revealing that only a small amount of data is required to learn style and format and exposure to acquired pretraining knowledge and capabilities.

8. Ethical considerations and limitations of the approach are also addressed in the paper, emphasizing the importance of defining more tasks and formats and extending R E A LIGN to multi-turn conversations.

9. The authors have made the associated code and data publicly accessible to support future studies at https://github.com/GAIR-NLP/Re....

Summary

Introduction to REALIGN
The research paper introduces the method named REALIGN, which aims to enhance the quality of finetuning data for large language models (LLMs). The paper discusses the reformulation of responses from instruction data to align better with pre-established criteria and evidence, thus reducing human annotation, hallucination, and scalability issues. REALIGN significantly improves the general alignment ability, math reasoning, factuality, and readability of LLMs based on experimental findings. The paper also emphasizes the need for further research into the science and mechanistic interpretability of LLMs. The associated code and data have been made publicly accessible to support future studies.

Significance of REALIGN
Furthermore, the paper discusses the significance of instruction data quality and the limitations of current methods in improving it. It introduces REALIGN as a simple and effective approach to improving the quality of existing instruction data, without the need for human annotation or extensive data. The method involves criteria definition, retrieval augmentation, and reformatting to align responses with the pre-established criteria and evidence, thus creating more contextually precise and aligned responses with human preferences.

The paper provides experimental results demonstrating that REALIGN significantly boosts general alignment ability, math reasoning, factuality, and readability without the need for additional data or advanced training techniques. It also presents findings on the impact of REALIGN data percentage on general alignment ability and knowledge ability. The paper also conducts an ablation study, scaling law analysis, and case studies to further support the effectiveness of REALIGN. Additionally, it acknowledges some limitations of the approach, including the need for improved reformatting models, expanded task categories, and an extension of REALIGN to multi-turn conversations.

Conclusion and Future Directions
In conclusion, the paper emphasizes the ethical considerations, provides a detailed overview of the REALIGN approach, and highlights the release of code and data for future research.

Reference: https://arxiv.org/abs/2402.122...