Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations
Existing LLM hallucination mitigation methods either fail to alter internal knowledge or lack cross-domain generalization. Contrastive decoding uses layer-wise differences but has only been studied in transformers. This research investigates mixture-of-experts (MoE) models, finding that while shared-expert MoEs lack layer-wise differences, higher layers exhibit distinct expert activation patterns between factual and non-factual outputs. The proposed EAACD method splits high-layer experts into reliability groups, contrasts their predictions, and amplifies hallucinations from lower-reliability experts as negative references, outperforming baselines on four QA datasets.
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[Submitted on 8 May 2026]
Title:Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations
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Abstract:Existing LLM hallucination mitigation methods, including prompt engineering and model optimization, either hardly alter models'internal knowledge or have poor cross-domain generalization. Contrastive decoding mitigates hallucinations by using layer-wise differences in LLMs. However, prior studies only explore transformer-based models (e.g., GPT), ignoring other effective frameworks like mixture-of-experts (MoE) models. Since MoE alters the traditional transformer architecture, we conduct empirical studies to investigate whether similar layer-wise differences exist in MoEs. Our results show that they do not exist in MoE with shared experts; nevertheless, across different MoEs, higher layers exhibit distinct expert activation patterns between factual and non-factual outputs. Building on these, we propose EAACD, an expert-aware adaptive contrast decoding that uses expert differences in MoE's higher layers to mitigate hallucinations on QA tasks. EAACD splits high-layer experts into a higher-reliability group and several lower-reliability groups based on their confidence and consistency. It contrasts the higher-reliability group's prediction with each lower-reliability group's prediction to calibrate the model's original predictions. To strengthen this contrast, EAACD amplifies hallucinations from lower-reliability experts via attention and masking to provide stronger negative references. EAACD outperforms all baselines on four datasets.
Comments: Accepted by ACL2
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.20426 [cs.CL]
(or arXiv:2607.20426v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20426
arXiv-issued DOI via DataCite
Submission history
From: Fang Xinyue [view email] [v1] Fri, 8 May 2026 16:30:03 UTC (1,084 KB)
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