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@@ -41,13 +41,7 @@ Apart from that, one can also use PairRM to further align instruction-tuned LLMs
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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 [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large), and thus it is super efficient: 0.4B.
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- We trained PairRM on a diverse collection of six human-preference datasets:
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- - [`UltraFeedback`](https://huggingface.co/datasets/openbmb/UltraFeedback)
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- - [`HH-RLHF`](https://huggingface.co/datasets/Anthropic/hh-rlhf)
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- - [`summarize_from_feedback`](https://huggingface.co/datasets/openai/summarize_from_feedback)
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- - [`chatbot_arena_conversations`](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
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- - [`webgpt_comparisons`](https://huggingface.co/datasets/openai/webgpt_comparisons)
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- - [`instruct-synthetic-prompt-responses`](https://huggingface.co/datasets/Dahoas/instruct-synthetic-prompt-responses).
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  PairRM is part of the LLM-Blender project (ACL 2023). Please see our [paper](https://arxiv.org/abs/2306.02561) above to know more.
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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 [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large), and thus it is super efficient: 0.4B.
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+ We trained PairRM on a diverse collection of six human-preference datasets (see more [here](https://huggingface.co/llm-blender/PairRM#training-datasets)).
 
 
 
 
 
 
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  PairRM is part of the LLM-Blender project (ACL 2023). Please see our [paper](https://arxiv.org/abs/2306.02561) above to know more.
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