  We present Second Thought, 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 Thought 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.
