@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}
}
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.
ContextCite: Attributing Model Generation to Context
Benjamin Cohen-Wang Harshay Shah Kristian Georgiev Aleksander Madry