The quality of finetuning data is crucial for aligning large language models (LLMs)  with human values.
Current methods to improve data quality are either labor-intensive or prone to factual errors caused by LLM hallucinations.
This paper explores elevating the quality of existing instruction data to better align with human values, introducing a simple and effective approach namedReAlign, whichreformatsthe responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence.
This approach minimizes human annotation, hallucination, and the difficulty in scaling, remaining orthogonal to existing alignment techniques.
Experimentally,ReAlignsignificantly boosts the general alignment ability, math reasoning, factuality, and readability of the LLMs. Encouragingly,withoutintroducing any additional data or advanced training techniques, and merely by reformatting the response, LLaMA-2-13B’s mathematical reasoning ability onGSM8Kcan be improvedfrom 46.77% to 56.63%in accuracy.
Additionally, a mere 5% ofReAligndata yields a 67% boost in general alignment ability measured by the Alpaca dataset.
This work highlights the need for further research into thescienceandmechanistic interpretabilityof LLMs. We have made the associated code and data publicly accessible to support future studies athttps://github.com/GAIR-NLP/ReAlign.