Ask, Assess, and Refine: Rectifying Factual Consistency and Hallucination in LLMs with Metric-Guided Feedback Learning (2024.eacl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have heralded unprecedented capabilities in information seeking and text generation, but challenges remain regarding citation errors and generating information not present in the evidence (hallucination). |
| Approach: | They propose a framework to assess citation errors and hallucination using an explicit evaluation paradigm to formulate actionable natural language feedback. |
| Outcome: | The proposed approach improves correctness, fluency, and citation quality and reduces hallucinations in the results. |
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