Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits

Ruibo Liu · Chenyan Jia · Ge Zhang · Ziyu Zhuang · Tony Liu · Soroush Vosoughi

Hall J #925

Keywords: [ AI safety ] [ human values ] [ social impact ] [ human-AI interaction ] [ alignment ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Tue 29 Nov 2 p.m. PST — 4 p.m. PST


We present Second Thoughts, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thoughts not only achieves superior performance in three value alignment benchmark datasets but also shows strong human-value transfer learning ability in few-shot scenarios. The generated editing steps also offer better interpretability and ease for interactive error correction. Extensive human evaluations further confirm its effectiveness.

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