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2023

SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Zhao, Yao, Joshi, Rishabh, Liu, Tianqi et al.

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Learning from human feedback has been shown to be effective at aligning language models with human preferences.

Past work has often relied on Reinforcement Learning from Human Feedback (RLHF), which optimizes the language model using reward scores assigned from a reward model trained on human preference data.
In this work we show how the recently introduced Sequence Likelihood Calibration (SLiC), can also be used to effectively learn from human preferences (SLiC-HF).
Furthermore, we demonstrate this can be done with human feedback data collected for a different model, similar to off-policy, offline RL data.

SLiC-HF: Sequence Likelihood Calibration with Human Feedback · Around

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