Papers by Yanbo Zhai

2 papers
Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization (2026.findings-acl)

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Challenge: Hallucinations in Large Language Models persist in critical domains where generated content diverges from contextual facts or logical constraints.
Approach: They propose to generate hallucinations as orthogonal noise relative to the semantic manifold of the residual stream.
Outcome: The proposed method achieves superior contextual faithfulness compared to state-of-the-art methods.
Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations (2026.acl-long)

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Challenge: Sparse Mixture-of-Experts models are vulnerable to hallucinations, authors say . static Top-k routing leaves "specialist experts" under-prioritized for specific tokens .
Approach: They propose a training-free inference framework to awaken dormant experts . they propose 'counterfactual routing' to shift computational resources from syntax-dominant to knowledge-intensive layers .
Outcome: Experiments show that CoR improves factual accuracy by 3.1% without increasing the inference budget.

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