Papers by Yining Sun
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting (2022.naacl-main)
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Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, Daxin Jiang
| Challenge: | Existing knowledge-grounded dialogue generation models only produce pedantic responses, which lacks emotion and attraction compared with the responses with polite style, positive and negative sentiments. |
| Approach: | They propose a method which generates responses via combing disentangled style templates and content templates. |
| Outcome: | The proposed method improves on evaluation metrics compared with state-of-the-art methods. |
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024.emnlp-demo)
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Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, Xiaoyin Che, Zhiyuan Liu, Maosong Sun
| Challenge: | Xia et al., 2018) demonstrate that a large language model can generate and maintain high-quality code documentation. |
| Approach: | They propose a large language model powered open-source framework for generating, maintaining, and updating code documentation. |
| Outcome: | The proposed framework generates high-quality documentation for the entire project. |
Rethinking RL Evaluation: Can Benchmarks Truly Reveal Failures of RL Methods? (2026.findings-acl)
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| Challenge: | Existing benchmarks for reinforcement learning for large language models do not accurately assess generalization. |
| Approach: | They propose three core principles for designing more faithful benchmarks: sufficient difficulty, balanced evaluation, and distributional robustness. |
| Outcome: | The proposed benchmarks do not accurately assess generalization across distribution shifts, difficulty levels, and counterfactual scenarios. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
Going “Deeper”: Structured Sememe Prediction via Transformer with Tree Attention (2022.findings-acl)
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| Challenge: | Existing studies ignore hierarchical structures of sememes in sememe-based semantic description systems. |
| Approach: | They propose a structured sememe prediction problem to predict a sememes tree with hierarchical structures rather than a set of sememas. |
| Outcome: | The proposed model outperforms baseline models and shows its effectiveness . it predicts a sememe tree with hierarchical structures rather than a set of sememes . |
FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights (2025.findings-acl)
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| Challenge: | Existing approaches to automate scientific research are limited by human cognitive constraints and timeintensive workflows. |
| Approach: | They propose a framework that enhances medical paper generation through iterative refinement and structured feedback. |
| Outcome: | The proposed framework achieves significant improvements over conventional methods across multiple models and evaluation dimensions. |
DebugBench: Evaluating Debugging Capability of Large Language Models (2024.findings-acl)
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Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Hui Haotian, Liu Weichuan, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language models (LLMs) have demonstrated exceptional coding capabilities, but their debugging capabilities remain relatively unexplored. |
| Approach: | They propose a debugging benchmark consisting of 4,253 LLMs with four major bug categories and 18 minor types in C++, Java, and Python. |
| Outcome: | The proposed benchmark covers four major bug categories and 18 minor types in C++, Java, and Python. |
The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) aligned via outcome-based Reinforcement Learning (RL) exhibit a critical failure mode: they exhibit brittle reasoning capabilities on out-of-distribution tasks. |
| Approach: | They propose a framework bridging Structural Causal Models and the Information Bottleneck principle to explain this paradox. |
| Outcome: | The proposed framework bridges the framework between SCM and IB principles to explain the problem. |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |