@inproceedings{zhang-etal-2025-law,
title = "The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing {LLM} Hallucination",
author = "Zhang, Yuji and
Li, Sha and
Qian, Cheng and
Liu, Jiateng and
Yu, Pengfei and
Han, Chi and
Fung, Yi R. and
McKeown, Kathleen and
Zhai, ChengXiang and
Li, Manling and
Ji, Heng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1199/",
doi = "10.18653/v1/2025.findings-acl.1199",
pages = "23340--23358",
ISBN = "979-8-89176-256-5",
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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<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.</abstract>
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%0 Conference Proceedings
%T The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination
%A Zhang, Yuji
%A Li, Sha
%A Qian, Cheng
%A Liu, Jiateng
%A Yu, Pengfei
%A Han, Chi
%A Fung, Yi R.
%A McKeown, Kathleen
%A Zhai, ChengXiang
%A Li, Manling
%A Ji, Heng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-law
%X 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.
%R 10.18653/v1/2025.findings-acl.1199
%U https://aclanthology.org/2025.findings-acl.1199/
%U https://doi.org/10.18653/v1/2025.findings-acl.1199
%P 23340-23358
Markdown (Informal)
[The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination](https://aclanthology.org/2025.findings-acl.1199/) (Zhang et al., Findings 2025)
ACL
- Yuji Zhang, Sha Li, Cheng Qian, Jiateng Liu, Pengfei Yu, Chi Han, Yi R. Fung, Kathleen McKeown, ChengXiang Zhai, Manling Li, and Heng Ji. 2025. The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23340–23358, Vienna, Austria. Association for Computational Linguistics.