Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis, Solution, and Interpretation

arXiv — cs.CL•Wednesday, November 5, 2025 at 5:00:00 AM

The article titled "Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis, Solution, and Interpretation" addresses the issue of factual hallucinations that large language models (LLMs) may exhibit when fine-tuned with new knowledge. It emphasizes the importance of gaining a deeper understanding of how these hallucinations arise and the mechanisms behind them. To facilitate this analysis, the authors introduce a controlled dataset named Biography-Reasoning, designed specifically to investigate these phenomena. This work supports the claim that a more thorough exploration is necessary to mitigate such hallucinations effectively. The study contributes to ongoing research efforts focused on improving the reliability and factual accuracy of LLMs, as reflected in related recent literature. By providing both analytical insights and practical tools, the article aims to advance the development of more trustworthy language models.

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