Papers by Katie Kang
Unfamiliar Finetuning Examples Control How Language Models Hallucinate (2025.naacl-long)
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| Challenge: | Large language models (LLMs) generate plausible-sounding responses that are factually incorrect. |
| Approach: | They propose an approach to learn more reliable reward models by modifying how unfamiliar finetuning examples are supervised to influence model responses to unfamiliar queries. |
| Outcome: | The proposed approach improves the efficacy of RL factuality finetuning in long-form biography and book/movie plot generation tasks. |