Context and Layers in Harmony: A Unified Strategy for Mitigating LLM Hallucinations

Sangyeon Yu, Gyunyeop Kim () and Sangwoo Kang ()
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Sangyeon Yu: School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea
Gyunyeop Kim: School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea
Sangwoo Kang: School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea

Mathematics, 2025, vol. 13, issue 11, 1-16

Abstract: Large language models, despite their strong performance, frequently produce hallucinated content due to excessive reliance on pre-trained knowledge while insufficiently integrating newly provided context. We introduce LACD, a technique that dynamically rebalances probability distributions across layers, ensuring critical context is not overshadowed. By emphasizing new prompt information, LACD alleviates lower-layer dominance and mitigates hallucinations. On the HotPotQA dataset, LACD outperforms basic context injection baselines by approximately 2.2% in exact match (EM) and matches or exceeds advanced methods such as DoLa and CAD. LACD also demonstrates robust gains on SQuAD, underscoring its capacity to reduce hallucinations while improving factual consistency. Overall, these findings highlight the importance of carefully integrating newly provided context with pre-trained knowledge to achieve more reliable language generation.

Keywords: large language models; hallucination; decoding strategy; dynamic layer selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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