The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination

• URL: http://arxiv.org/abs/2502.16143v1
• Date: Sat, 22 Feb 2025 08:36:06 GMT
• Title: The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination
• Authors: Yuji Zhang, Sha Li, Cheng Qian, Jiateng Liu, Pengfei Yu, Chi Han, Yi R. Fung, Kathleen McKeown, Chengxiang Zhai, Manling Li, Heng Ji,
• Abstract summary: We introduce a novel framework to quantify factual hallucinations by modeling knowledge overshadowing.<n>We propose a new decoding strategy CoDa, to mitigate hallucinations, which notably enhance model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%)
• Score: 85.18584652829799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hallucination is a persistent challenge in large language models (LLMs), where even with rigorous quality control, models often generate distorted facts. This paradox, in which error generation continues despite high-quality training data, calls for a deeper understanding of the underlying LLM mechanisms. To address it, we propose a novel concept: knowledge overshadowing, where model's dominant knowledge can obscure less prominent knowledge during text generation, causing the model to fabricate inaccurate details. Building on this idea, we introduce a novel framework to quantify factual hallucinations by modeling knowledge overshadowing. Central to our approach is the log-linear law, which predicts that the rate of factual hallucination increases linearly with the logarithmic scale of (1) Knowledge Popularity, (2) Knowledge Length, and (3) Model Size. The law provides a means to preemptively quantify hallucinations, offering foresight into their occurrence even before model training or inference. Built on overshadowing effect, we propose a new decoding strategy CoDa, to mitigate hallucinations, which notably enhance model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%). Our findings not only deepen understandings of the underlying mechanisms behind hallucinations but also provide actionable insights for developing more predictable and controllable language models.

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