SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models

• URL: http://arxiv.org/abs/2502.09604v1
• Date: Thu, 13 Feb 2025 18:55:13 GMT
• Title: SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
• Authors: Yung-Sung Chuang, Benjamin Cohen-Wang, Shannon Zejiang Shen, Zhaofeng Wu, Hu Xu, Xi Victoria Lin, James Glass, Shang-Wen Li, Wen-tau Yih,
• Abstract summary: SelfCite is a self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for statements in generated responses. Instead of relying on costly and labor-intensive annotations, SelfCite leverages a reward signal provided by the LLM itself through context ablation. The effectiveness of SelfCite is demonstrated by increasing citation F1 up to 5.3 points on the LongBench-Cite benchmark across five long-form question answering tasks.
• Score: 51.90867482317985
• License:
• Abstract: We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated responses. Instead of only relying on costly and labor-intensive annotations, SelfCite leverages a reward signal provided by the LLM itself through context ablation: If a citation is necessary, removing the cited text from the context should prevent the same response; if sufficient, retaining the cited text alone should preserve the same response. This reward can guide the inference-time best-of-N sampling strategy to improve citation quality significantly, as well as be used in preference optimization to directly fine-tune the models for generating better citations. The effectiveness of SelfCite is demonstrated by increasing citation F1 up to 5.3 points on the LongBench-Cite benchmark across five long-form question answering tasks.

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