Papers by Lingxiang Wang
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. |
Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation (2025.findings-emnlp)
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| Challenge: | Existing retrieval models emphasize surface-level semantic similarity, neglecting deeper solution-level logical similarities. |
| Approach: | They propose a solution-aware ranking model empowered by synthetic data for competitive programming tasks. |
| Outcome: | The proposed ranking model outperforms existing retrieval models in precision and recall metrics. |
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. |
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model (2024.emnlp-main)
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| Challenge: | Existing large language models (LLMs) use large amounts of public data and massive parameters, but private data is often stored in isolated data silos. |
| Approach: | They propose a Federated Learning framework for large language models which offloads most training parameters to the server while training embedding and output layers locally. |
| Outcome: | The proposed framework achieves comparable metrics to centralized chatGLM model on NLU and generation tasks. |