ContextCite: Attributing Model Generation to Context
Authors: name: Benjamin Cohen-Wang, name: Harshay Shah, name: Kristian Georgiev, name: 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.