Pairwise preferences over model responses are widely collected to evaluate and provide feedback to large language models (LLMs). Given two alternative model responses to the same input, a human or AI annotator selects the “better” response. Such data can provide a feedback signal in domains where traditional hard-coded metrics are difficult to obtain (e.g. quality of a chat interactions), thereby helping measure model progress or model fine-tuning (e.g., via reinforcement learning from human feedback, RLHF). However, for some domains it can be tricky to obtain such pairwise comparisons in…
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