How do language models actually 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 two case studies: (1) automatically verifying statements based on the attributed parts of the context and (2) improving response quality by extracting query-relevant information from the context.