SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2410.11315v1
• Date: Tue, 15 Oct 2024 06:26:24 GMT
• Title: SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation
• Authors: Xinping Zhao, Dongfang Li, Yan Zhong, Boren Hu, Yibin Chen, Baotian Hu, Min Zhang,
• Abstract summary: We propose a model-based evidence extraction learning framework, SEER, to optimize a vanilla model as an evidence extractor. Our method largely improves the final RAG performance, enhances the faithfulness, helpfulness, and conciseness of the extracted evidence, and reduces the evidence length by 9.25 times.
• Score: 21.823931225182115
• License:
• Abstract: Recent studies in Retrieval-Augmented Generation (RAG) have investigated extracting evidence from retrieved passages to reduce computational costs and enhance the final RAG performance, yet it remains challenging. Existing methods heavily rely on heuristic-based augmentation, encountering several issues: (1) Poor generalization due to hand-crafted context filtering; (2) Semantics deficiency due to rule-based context chunking; (3) Skewed length due to sentence-wise filter learning. To address these issues, we propose a model-based evidence extraction learning framework, SEER, optimizing a vanilla model as an evidence extractor with desired properties through self-aligned learning. Extensive experiments show that our method largely improves the final RAG performance, enhances the faithfulness, helpfulness, and conciseness of the extracted evidence, and reduces the evidence length by 9.25 times. The code will be available at https://github.com/HITsz-TMG/SEER.

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