Decomposing and Revising What Language Models Generate

• URL: http://arxiv.org/abs/2509.00765v1
• Date: Sun, 31 Aug 2025 09:26:25 GMT
• Title: Decomposing and Revising What Language Models Generate
• Authors: Zhichao Yan, Jiaoyan Chen, Jiapu Wang, Xiaoli Li, Ru Li, Jeff Z. Pan,
• Abstract summary: We propose a new fact decomposition-based framework called FIDES for attributed QA.<n>Fides uses a contextually enhanced two-stage faithful decomposition method to decompose long form answers into sub-facts.<n>If the retrieved evidence snippets conflict with the related sub-facts, such sub-facts will be revised accordingly.<n>Fides outperforms the SOTA methods by over 14% in average with GPT-3.5-turbo, Gemini and Llama 70B series.
• Score: 29.67882325906939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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