Incorporating Q&A Nuggets into Retrieval-Augmented Generation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914426259505152 |
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| author | Dietz, Laura Li, Bryan Liu, Gabrielle Ju, Jia-Huei Yang, Eugene Lawrie, Dawn Walden, William Mayfield, James |
| author_facet | Dietz, Laura Li, Bryan Liu, Gabrielle Ju, Jia-Huei Yang, Eugene Lawrie, Dawn Walden, William Mayfield, James |
| contents | RAGE systems integrate ideas from automatic evaluation (E) into Retrieval-augmented Generation (RAG). As one such example, we present Crucible, a Nugget-Augmented Generation System that preserves explicit citation provenance by constructing a bank of Q&A nuggets from retrieved documents and uses them to guide extraction, selection, and report generation. Reasoning on nuggets avoids repeated information through clear and interpretable Q&A semantics - instead of opaque cluster abstractions - while maintaining citation provenance throughout the entire generation process. Evaluated on the TREC NeuCLIR 2024 collection, our Crucible system substantially outperforms Ginger, a recent nugget-based RAG system, in nugget recall, density, and citation grounding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13222 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Incorporating Q&A Nuggets into Retrieval-Augmented Generation Dietz, Laura Li, Bryan Liu, Gabrielle Ju, Jia-Huei Yang, Eugene Lawrie, Dawn Walden, William Mayfield, James Information Retrieval Artificial Intelligence H.3 RAGE systems integrate ideas from automatic evaluation (E) into Retrieval-augmented Generation (RAG). As one such example, we present Crucible, a Nugget-Augmented Generation System that preserves explicit citation provenance by constructing a bank of Q&A nuggets from retrieved documents and uses them to guide extraction, selection, and report generation. Reasoning on nuggets avoids repeated information through clear and interpretable Q&A semantics - instead of opaque cluster abstractions - while maintaining citation provenance throughout the entire generation process. Evaluated on the TREC NeuCLIR 2024 collection, our Crucible system substantially outperforms Ginger, a recent nugget-based RAG system, in nugget recall, density, and citation grounding. |
| title | Incorporating Q&A Nuggets into Retrieval-Augmented Generation |
| topic | Information Retrieval Artificial Intelligence H.3 |
| url | https://arxiv.org/abs/2601.13222 |