Unfamiliar Finetuning Examples Control How Language Models Hallucinate
Overview
Paper Summary
This paper finds that unfamiliar examples in an LLM's finetuning data significantly influence its hallucinations, with the model's predictions mirroring responses associated with these examples. This suggests that manipulating the finetuning data could steer the model towards more desirable responses, like expressing uncertainty when it doesn't know.
Explain Like I'm Five
When large language models (LLMs) make things up, they often repeat stuff they learned during training, even if it's wrong. By changing their training, we can make them better at admitting when they don't know something.
Possible Conflicts of Interest
None identified
Identified Limitations
Rating Explanation
This paper presents a novel perspective on how LLMs hallucinate and offers a potential solution through conservative reward models. While the focus on QA tasks and the limited scope of unfamiliarity are limitations, the core findings and the proposed approach are valuable contributions to the field.
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