Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910966160031744 |
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| author | Sharifymoghaddam, Sahel Upadhyay, Shivani Thakur, Nandan Pradeep, Ronak Lin, Jimmy |
| author_facet | Sharifymoghaddam, Sahel Upadhyay, Shivani Thakur, Nandan Pradeep, Ronak Lin, Jimmy |
| contents | Battles, or side-by-side comparisons in so-called arenas that elicit human preferences, have emerged as a popular approach for assessing the output quality of LLMs. Recently, this idea has been extended to retrieval-augmented generation (RAG) systems. While undoubtedly representing an advance in evaluation, battles have at least two drawbacks, particularly in the context of complex information-seeking queries: they are neither explanatory nor diagnostic. Recently, the nugget evaluation methodology has emerged as a promising approach to evaluate the quality of RAG answers. Nuggets decompose long-form LLM-generated answers into atomic facts, highlighting important pieces of information necessary in a "good" response. In this work, we apply our AutoNuggetizer framework to analyze data from roughly 7K Search Arena battles provided by LMArena in a fully automatic manner. Our results show a significant correlation between nugget scores and human preferences, showcasing promise in our approach to explainable and diagnostic system evaluations. All the code necessary to reproduce results in our work is available in https://github.com/castorini/lmsys_nuggetize. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_20006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses Sharifymoghaddam, Sahel Upadhyay, Shivani Thakur, Nandan Pradeep, Ronak Lin, Jimmy Information Retrieval Battles, or side-by-side comparisons in so-called arenas that elicit human preferences, have emerged as a popular approach for assessing the output quality of LLMs. Recently, this idea has been extended to retrieval-augmented generation (RAG) systems. While undoubtedly representing an advance in evaluation, battles have at least two drawbacks, particularly in the context of complex information-seeking queries: they are neither explanatory nor diagnostic. Recently, the nugget evaluation methodology has emerged as a promising approach to evaluate the quality of RAG answers. Nuggets decompose long-form LLM-generated answers into atomic facts, highlighting important pieces of information necessary in a "good" response. In this work, we apply our AutoNuggetizer framework to analyze data from roughly 7K Search Arena battles provided by LMArena in a fully automatic manner. Our results show a significant correlation between nugget scores and human preferences, showcasing promise in our approach to explainable and diagnostic system evaluations. All the code necessary to reproduce results in our work is available in https://github.com/castorini/lmsys_nuggetize. |
| title | Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2504.20006 |