ContextCite: Attributing Model Generation to Context
Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry
Abstract
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.
BibTeX
@inproceedings{
cohen-wang2024contextcite,
title={ContextCite: Attributing Model Generation to Context},
author={Benjamin Cohen-Wang and Harshay Shah and Kristian Georgiev and Aleksander Madry},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7CMNSqsZJt}
}