ContextCite: Attributing Model Generation to Context

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.

Authors

• Benjamin Cohen-Wang
• Harshay Shah
• Kristian Georgiev
• Aleksander Mądry

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