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cs.CL2025
Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation
Hongxiang Zhang, Hao Chen, Muhao Chen +1
Recent decoding methods improve the factuality of large language models (LLMs) by refining how the next token is selected during generation. These methods typically operate at the…
cs.CL2025
MiCEval: Unveiling Multimodal Chain of Thought's Quality via Image Description and Reasoning Steps
Xiongtao Zhou, Jie He, Lanyu Chen +5
Multimodal Chain of Thought (MCoT) is a popular prompting strategy for improving the performance of multimodal large language models (MLLMs) across a range of complex reasoning tas…
cs.CL2025
On Fairness of Unified Multimodal Large Language Model for Image Generation
Ming Liu, Hao Chen, Jindong Wang +3
Unified multimodal large language models (U-MLLMs) have demonstrated impressive performance in visual understanding and generation in an end-to-end pipeline. Compared with generati…