Papers by Eunkyeong Lee
Synthetic Paths to Integral Truth: Mitigating Hallucinations Caused by Confirmation Bias with Synthetic Data (2025.coling-main)
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| Challenge: | Existing methods to mitigate confirmation bias-induced hallucination in large language models (LLMs) however, they still exhibit issues such as confirmation bias, which remains unexplored in current research. |
| Approach: | They propose a method to mitigate confirmation bias-induced hallucination in large language models by using a synthetic data construction pipeline and direct preference optimization (DPO) training. |
| Outcome: | The proposed method improves response accuracy and reduced hallucination on natural questions open and halubench. |