Concise and Sufficient Sub-Sentence Citations for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2509.20859v2
• Date: Sat, 18 Oct 2025 16:18:53 GMT
• Title: Concise and Sufficient Sub-Sentence Citations for Retrieval-Augmented Generation
• Authors: Guo Chen, Qiuyuan Li, Qiuxian Li, Hongliang Dai, Xiang Chen, Piji Li,
• Abstract summary: In RAG question answering systems, generating citations for large language model (LLM) outputs enhances verifiability and helps users identify potential hallucinations.<n>First, the citations are typically provided at the sentence or even paragraph level.<n>Second, sentence-level citations may omit information that is essential for verifying the output, forcing users to read the surrounding context.<n>We propose generating sub-sentence citations that are both concise and sufficient, thereby reducing the effort required by users to confirm the correctness of the generated output.
• Score: 28.229130944067787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In retrieval-augmented generation (RAG) question answering systems, generating citations for large language model (LLM) outputs enhances verifiability and helps users identify potential hallucinations. However, we observe two problems in the citations produced by existing attribution methods. First, the citations are typically provided at the sentence or even paragraph level. Long sentences or paragraphs may include a substantial amount of irrelevant content. Second, sentence-level citations may omit information that is essential for verifying the output, forcing users to read the surrounding context. In this paper, we propose generating sub-sentence citations that are both concise and sufficient, thereby reducing the effort required by users to confirm the correctness of the generated output. To this end, we first develop annotation guidelines for such citations and construct a corresponding dataset. Then, we propose an attribution framework for generating citations that adhere to our standards. This framework leverages LLMs to automatically generate fine-tuning data for our task and employs a credit model to filter out low-quality examples. Our experiments on the constructed dataset demonstrate that the propose approach can generate high-quality and more readable citations.

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