This podcast episode discusses a research paper focused on making large language models (LLMs) safer and better aligned with human values. The authors introduce a new technique called DATA ADVISOR, which helps LLMs create safer and more reliable data by following guiding principles. DATA ADVISOR works by continuously reviewing the data generated by the model, spotting gaps or issues, and suggesting improvements for the next round of data creation. The study shows that this method makes LLMs safer without reducing their overall effectiveness, and it performs better than other current approaches for generating safer data.

Original paper:

Wang, F., Mehrabi, N., Goyal, P., Gupta, R., Chang, K.-W., & Galstyan, A. (2024). Data Advisor: Dynamic data curation for safety alignment of large language models. arXiv. https://arxiv.org/abs/2410.05269