Summary of Contextcite: Attributing Model Generation to Context, by Benjamin Cohen-wang et al.
ContextCite: Attributing Model Generation to Context
by Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a method called ContextCite to determine whether a language model’s generated statement is grounded in the provided context, misinterpreted, or fabricated. The approach can be applied on top of any existing language model and is demonstrated through three applications: verifying generated statements, improving response quality by pruning the context, and detecting poisoning attacks. The authors introduce the problem of context attribution, which involves pinpointing the parts of the context that led to a particular statement being generated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how language models use information provided as context when generating responses. It’s like trying to figure out what part of the story made the model say something specific. The researchers come up with a way called ContextCite to find out if the model is using the right context, getting it wrong, or making things up. They show how this can be useful in three ways: checking if generated statements are true, making responses better by removing unnecessary context, and spotting when someone tries to trick the model. |
Keywords
» Artificial intelligence » Language model » Pruning
