Decomposing and Revising What Language Models Generate
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909900957810688 |
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| author | Yan, Zhichao Chen, Jiaoyan Wang, Jiapu Li, Xiaoli Li, Ru Pan, Jeff Z. |
| author_facet | Yan, Zhichao Chen, Jiaoyan Wang, Jiapu Li, Xiaoli Li, Ru Pan, Jeff Z. |
| contents | Attribution is crucial in question answering (QA) with Large Language Models (LLMs).SOTA question decomposition-based approaches use long form answers to generate questions for retrieving related documents. However, the generated questions are often irrelevant and incomplete, resulting in a loss of facts in retrieval.These approaches also fail to aggregate evidence snippets from different documents and paragraphs. To tackle these problems, we propose a new fact decomposition-based framework called FIDES (\textit{faithful context enhanced fact decomposition and evidence aggregation}) for attributed QA. FIDES uses a contextually enhanced two-stage faithful decomposition method to decompose long form answers into sub-facts, which are then used by a retriever to retrieve related evidence snippets. If the retrieved evidence snippets conflict with the related sub-facts, such sub-facts will be revised accordingly. Finally, the evidence snippets are aggregated according to the original sentences.Extensive evaluation has been conducted with six datasets, with an additionally proposed new metric called $Attr_{auto-P}$ for evaluating the evidence precision. FIDES outperforms the SOTA methods by over 14\% in average with GPT-3.5-turbo, Gemini and Llama 70B series. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00765 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decomposing and Revising What Language Models Generate Yan, Zhichao Chen, Jiaoyan Wang, Jiapu Li, Xiaoli Li, Ru Pan, Jeff Z. Computation and Language Attribution is crucial in question answering (QA) with Large Language Models (LLMs).SOTA question decomposition-based approaches use long form answers to generate questions for retrieving related documents. However, the generated questions are often irrelevant and incomplete, resulting in a loss of facts in retrieval.These approaches also fail to aggregate evidence snippets from different documents and paragraphs. To tackle these problems, we propose a new fact decomposition-based framework called FIDES (\textit{faithful context enhanced fact decomposition and evidence aggregation}) for attributed QA. FIDES uses a contextually enhanced two-stage faithful decomposition method to decompose long form answers into sub-facts, which are then used by a retriever to retrieve related evidence snippets. If the retrieved evidence snippets conflict with the related sub-facts, such sub-facts will be revised accordingly. Finally, the evidence snippets are aggregated according to the original sentences.Extensive evaluation has been conducted with six datasets, with an additionally proposed new metric called $Attr_{auto-P}$ for evaluating the evidence precision. FIDES outperforms the SOTA methods by over 14\% in average with GPT-3.5-turbo, Gemini and Llama 70B series. |
| title | Decomposing and Revising What Language Models Generate |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.00765 |