Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908375757881344 |
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| author | Xia, Sirui Wang, Xintao Liang, Jiaqing Zhang, Yifei Zhou, Weikang Deng, Jiaji Yu, Fei Xiao, Yanghua |
| author_facet | Xia, Sirui Wang, Xintao Liang, Jiaqing Zhang, Yifei Zhou, Weikang Deng, Jiaji Yu, Fei Xiao, Yanghua |
| contents | Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. To enhance credibility and verifiability in RAG systems, Attributed Text Generation (ATG) is proposed, which provides citations to retrieval knowledge in LLM-generated responses. Prior methods mainly adopt coarse-grained attributions, with passage-level or paragraph-level references or citations, which fall short in verifiability. This paper proposes ReClaim (Refer & Claim), a fine-grained ATG method that alternates the generation of references and answers step by step. Different from previous coarse-grained attribution, ReClaim provides sentence-level citations in long-form question-answering tasks. With extensive experiments, we verify the effectiveness of ReClaim in extensive settings, achieving a citation accuracy rate of 90%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01796 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation Xia, Sirui Wang, Xintao Liang, Jiaqing Zhang, Yifei Zhou, Weikang Deng, Jiaji Yu, Fei Xiao, Yanghua Computation and Language Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. To enhance credibility and verifiability in RAG systems, Attributed Text Generation (ATG) is proposed, which provides citations to retrieval knowledge in LLM-generated responses. Prior methods mainly adopt coarse-grained attributions, with passage-level or paragraph-level references or citations, which fall short in verifiability. This paper proposes ReClaim (Refer & Claim), a fine-grained ATG method that alternates the generation of references and answers step by step. Different from previous coarse-grained attribution, ReClaim provides sentence-level citations in long-form question-answering tasks. With extensive experiments, we verify the effectiveness of ReClaim in extensive settings, achieving a citation accuracy rate of 90%. |
| title | Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.01796 |