ContextCite: Attributing Model Generation to Context

• URL: http://arxiv.org/abs/2409.00729v2
• Date: Fri, 13 Sep 2024 20:26:40 GMT
• Title: ContextCite: Attributing Model Generation to Context
• Abstract summary: We introduce the problem of context attribution, pinpointing the parts of the context that led a model to generate a particular statement. We then present ContextCite, a simple and scalable method for context attribution that can be applied on top of any existing language model. We showcase ContextCite through three applications: helping verify generated statements, improving response quality, and detecting poisoning attacks.
• Score: 64.90535024385305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How do language models use information provided as context when generating a response? Can we infer whether a particular generated statement is actually grounded in the context, a misinterpretation, or fabricated? To help answer these questions, we introduce the problem of context attribution: pinpointing the parts of the context (if any) that led a model to generate a particular statement. We then present ContextCite, a simple and scalable method for context attribution that can be applied on top of any existing language model. Finally, we showcase the utility of ContextCite through three applications: (1) helping verify generated statements (2) improving response quality by pruning the context and (3) detecting poisoning attacks. We provide code for ContextCite at https://github.com/MadryLab/context-cite.

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