LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914163132989440 |
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| author | Carmel, David Filice, Simone Horowitz, Guy Maarek, Yoelle Shtoff, Alex Somekh, Oren Tavory, Ran |
| author_facet | Carmel, David Filice, Simone Horowitz, Guy Maarek, Yoelle Shtoff, Alex Somekh, Oren Tavory, Ran |
| contents | With Retrieval Augmented Generation (RAG) becoming more and more prominent in generative AI solutions, there is an emerging need for systematically evaluating their effectiveness. We introduce the LiveRAG benchmark, a publicly available dataset of 895 synthetic questions and answers designed to support systematic evaluation of RAG-based Q&A systems. This synthetic benchmark is derived from the one used during the SIGIR'2025 LiveRAG Challenge, where competitors were evaluated under strict time constraints. It is augmented with information that was not made available to competitors during the Challenge, such as the ground-truth answers, together with their associated supporting claims which were used for evaluating competitors' answers. In addition, each question is associated with estimated difficulty and discriminability scores, derived from applying an Item Response Theory model to competitors' responses. Our analysis highlights the benchmark's questions diversity, the wide range of their difficulty levels, and their usefulness in differentiating between system capabilities. The LiveRAG benchmark will hopefully help the community advance RAG research, conduct systematic evaluation, and develop more robust Q&A systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluation Carmel, David Filice, Simone Horowitz, Guy Maarek, Yoelle Shtoff, Alex Somekh, Oren Tavory, Ran Computation and Language Information Retrieval With Retrieval Augmented Generation (RAG) becoming more and more prominent in generative AI solutions, there is an emerging need for systematically evaluating their effectiveness. We introduce the LiveRAG benchmark, a publicly available dataset of 895 synthetic questions and answers designed to support systematic evaluation of RAG-based Q&A systems. This synthetic benchmark is derived from the one used during the SIGIR'2025 LiveRAG Challenge, where competitors were evaluated under strict time constraints. It is augmented with information that was not made available to competitors during the Challenge, such as the ground-truth answers, together with their associated supporting claims which were used for evaluating competitors' answers. In addition, each question is associated with estimated difficulty and discriminability scores, derived from applying an Item Response Theory model to competitors' responses. Our analysis highlights the benchmark's questions diversity, the wide range of their difficulty levels, and their usefulness in differentiating between system capabilities. The LiveRAG benchmark will hopefully help the community advance RAG research, conduct systematic evaluation, and develop more robust Q&A systems. |
| title | LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluation |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2511.14531 |