Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track
Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry
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.