Papers by Linxi Su
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)
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Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong
| Challenge: | Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure. |
| Approach: | They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction. |
| Outcome: | The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models. |
C²RBench: A Chinese Complex Reasoning Benchmark for Large Language Models (2025.findings-acl)
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| Challenge: | Existing benchmarks often fail to capture complex multi-step reasoning demands inherent in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate multi-step, multimodal advanced reasoning of large language models. |
| Outcome: | The proposed benchmark exceeds existing benchmarks in cognitive complexity and accuracy by over 90% . it features 1,115 carefully curated Chinese tasks organized into eight domain-specific subsets . evaluations of 20 LLMs and 24 multimodal large language models reveal critical performance gaps . |