Papers by Yueyang Li
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization (2026.findings-acl)
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Yueyang Cang, Xiaoteng Zhang, Erlu Zhao, Zehua Ji, Yuhang Liu, Yuchen He, Zhiyuan Ning, Chen Yijun, Wenge Que, Li Shi
| Challenge: | Recent approaches to optimize communication topology rely on single-sample policy gradients with absolute rewards. |
| Approach: | They propose a topology optimization framework that integrates Group Relative Policy Optimization. |
| Outcome: | The proposed topology optimization framework outperforms state-of-the-art methods on reasoning and code generation benchmarks. |
LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Models (2025.findings-emnlp)
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| Challenge: | a problem of data contamination is now almost inevitable during the development of large language models, with the training data often integrating evaluation benchmarks even unintentionally. |
| Approach: | They propose a framework to restore model performance prior to data contamination on potentially leaked datasets by using contamination detection and disruption operation. |
| Outcome: | The proposed framework restores model performance prior to contamination on potentially leaked datasets. |
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning (2022.findings-naacl)
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| Challenge: | Pre-trained language models (PLMs) are a good starting point for downstream applications, but it is difficult to generalize them to new tasks given a few labeled samples. |
| Approach: | They propose to use Relation Graph augmented learning to improve the performance of few-shot natural language understanding tasks by rewriting the input sequence into a cloze question with masks. |
| Outcome: | Extensive experiments show that Relation Graph augmented learning (RGL) improves performance of prompt-based tuning strategies. |
DINT Transformer (2025.emnlp-main)
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| Challenge: | Experimental results show that the DINT Transformer improves accuracy and robustness across practical applications. |
| Approach: | They propose a differential attention mechanism that suppresses the impact of irrelevant contexts by computing DIF-Ference between two independent attention distributions. |
| Outcome: | The proposed architecture improves numerical stability and ability to capture global dependencies. |