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  Pairwise Reward Model (PairRM) takes an instruction and a **pair** of output candidates as the input,
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  and output a score for each candidate to measure their **relative** quality.
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- Unlike the other RMs that encode and score each candidate respectively,
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- PairRM takes a pair of candidates and compares them side-by-side to indentify the subtle differences between them.
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-
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  PairRM can be used to (re-)rank a list of candidate outputs and thus can be used an LLM evaluator to efficiently assess the quality of LLMs in local environment.
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  PairRM can also be used to enhance the decoding by `best-of-n sampling` (i.e., reranking N sampled outputs).
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  Apart from that, one can also use PairRM to further align instruction-tuned LLMs with RLHF methods.
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  PairRM is part of the LLM-Blender project (ACL 2023). Please see our paper linked above to know more.
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  Pairwise Reward Model (PairRM) takes an instruction and a **pair** of output candidates as the input,
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  and output a score for each candidate to measure their **relative** quality.
 
 
 
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  PairRM can be used to (re-)rank a list of candidate outputs and thus can be used an LLM evaluator to efficiently assess the quality of LLMs in local environment.
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  PairRM can also be used to enhance the decoding by `best-of-n sampling` (i.e., reranking N sampled outputs).
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  Apart from that, one can also use PairRM to further align instruction-tuned LLMs with RLHF methods.
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+ Unlike the other RMs that encode and score each candidate respectively,
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+ PairRM takes a pair of candidates and compares them side-by-side to indentify the subtle differences between them.
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+ Also, PairRM is based on DeBERTa-large, and thus it is super efficient: 0.4B.
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+ We trained PairRM on a diverse collection of human preference datasets such as UltraFeedback, HH-RLHF, chatbot-arena, etc.
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  PairRM is part of the LLM-Blender project (ACL 2023). Please see our paper linked above to know more.
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