ContextCite: Attributing Model Generation to Context
<|reference_start|>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.<|reference_end|>
@article{cohen-wang2024contextcite:,
    title={ContextCite: Attributing Model Generation to Context},
    author={Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry},
    journal={arXiv preprint arXiv:2409.00729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00729},
    primaryClass={cs.LG cs.CL}
}