Papers by Lingxiang Wu
OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)
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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang
| Challenge: | Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers. |
| Approach: | They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training. |
| Outcome: | The proposed model is open-source and transparent, with no data or data required to build it. |
C3D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding (2026.findings-acl)
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| Challenge: | Large language models are prone to distraction by contextual information during reasoning tasks. |
| Approach: | They propose a decoding method that uses predicted logits to estimate the model's confidence. |
| Outcome: | The proposed method reveals how the model dynamically activates and adjusts its consideration of each premise as reasoning progresses. |