Papers by Qiyan Zhao
TokenPenalty: Alleviating Attention Sinks and Positional Decay in LVLMs (2026.findings-acl)
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| Challenge: | Multimodal large language models (MLLMs) often hallucinate due to two relevant phenomena: massive activation phenomenon and positional information decay. |
| Approach: | They propose a token-level intervention strategy that dynamically suppresses irrelevant visual tokens while preserving key contextual signals. |
| Outcome: | Experiments show that TokenTruth significantly improves factual consistency across MLLMs on standard image understanding benchmarks. |
Fixing Semantic Blind Spots in Anchor Tokens of dMLLMs (2026.findings-acl)
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| Challenge: | Autoregressive models (ARMs) are prone to hallucinations due to their sequential text generation and high latency. |
| Approach: | They propose a training-free decoding strategy that augments the attention key space with a static, distance-aware matrix to reduce the attention sink effect on semantic anchors. |
| Outcome: | The proposed method reduces the attention sink effect on semantic anchors while enhancing their ability to aggregate global visual information. |