Papers by Weimin Xiong
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories (2024.findings-emnlp)
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Yifan Song, Weimin Xiong, Xiutian Zhao, Dawei Zhu, Wenhao Wu, Ke Wang, Cheng Li, Wei Peng, Sujian Li
| Challenge: | Existing studies focus on specialized agents designed for particular tasks. |
| Approach: | They propose to scale annotated interaction trajectories and fine-tune LLMs on AgentBank to get a series of agent models, Samoyed. |
| Outcome: | The proposed model can scale to get generalized agent capabilities. |
WIKIGENBENCH:Exploring Full-length Wikipedia Generation under Real-World Scenario (2025.coling-main)
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Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong, Dawei Zhu, Han Qian, Mingbo Song, Weimin Xiong, Xiaoguang Li, Qun Liu, Sujian Li
| Challenge: | Existing efforts to generate Wikipedia articles for new events fall short of real-world application. |
| Approach: | They propose a benchmark to generate Wikipedia articles for new events under real-world scenarios . they use systematic metrics and LLM-based metrics to assess verifiability, organization, and other aspects aligned with real-life scenarios. |
| Outcome: | The proposed benchmarks show that hierarchical-based methods generate more comprehensive content while fine-tuned methods achieve better verifiability. |
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective (2023.findings-emnlp)
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| Challenge: | Recent studies have identified the severe performance decrease on analogous classes as a key factor for catastrophic forgetting. |
| Approach: | They propose a replay-based continual text classification method that uses fast-slow and current-past contrastive learning to perform mutual information maximization and better recover previously learned representations. |
| Outcome: | The proposed method achieves state-of-the-art on three text classification tasks. |
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents (2025.naacl-industry)
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| Challenge: | Existing workflow construction methods require specialized knowledge and task-switching skills. |
| Approach: | They propose a multi-agent workflow framework that incorporates a supervisor, orchestrator, and filler agent. |
| Outcome: | The proposed framework significantly increases the success rate of workflow construction . the proposed framework is based on a dataset of 3,695 real-world business samples . |
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection (2025.coling-main)
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| Challenge: | Existing methods for personality detection ignore the connection between psychological knowledge “emotion regulation” and personality traits. |
| Approach: | They propose to use emotion regulation and emotion features to retrieve few-shot samples and provide process CoTs for inferring labels from text. |
| Outcome: | The proposed method outperforms SOTA by 15.05/4.29 on the two benchmark datasets. |
Watch Every Step! LLM Agent Learning via Iterative Step-level Process Refinement (2024.emnlp-main)
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| Challenge: | Recent approaches to enhance agent performance focus on outcome rewards, which may lead to errors or suboptimal actions due to the absence of process supervision signals. |
| Approach: | They propose a step-level framework that provides detailed step-by-step guidance to enhance agent training by using Monte Carlo methods. |
| Outcome: | The proposed framework outperforms strong baselines on three tasks and shows that it is effective in augmenting efficiency and its applicability to diverse models. |
MPO: Boosting LLM Agents with Meta Plan Optimization (2025.findings-emnlp)
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| Challenge: | Existing methods for interactive planning tasks suffer from planning hallucinations and require retraining for each new agent. |
| Approach: | They propose a framework that leverages explicit guidance through meta plans to assist agent planning and enables continuous optimization based on feedback from the agent’s task execution. |
| Outcome: | The proposed framework outperforms existing baselines on two representative tasks and significantly improves task completion efficiency and generalization capabilities. |
Rationale-Enhanced Language Models are Better Continual Relation Learners (2023.emnlp-main)
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| Challenge: | Recent studies have found that catastrophic forgetting arises from the model’s lack of robustness against future analogous relations. |
| Approach: | They propose a multi-task rationale tuning strategy to help the model learn current relations robustly and conduct contrastive rationale replay to further distinguish analogous relations. |
| Outcome: | The proposed method outperforms the state-of-the-art models on two benchmarks. |
PsyPath: Psychologically-guided Self-Exploration for Personality Detection (2026.findings-acl)
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| Challenge: | Personality detection aims to label traits via identifying linguistic cues from written text. |
| Approach: | They propose a framework that allows large language models to generate and answer psychologically meaningful questions and a hybrid scoring mechanism to evaluate the generated nodes in the reasoning paths. |
| Outcome: | The proposed framework outperforms baselines on two benchmark datasets and significantly improves performance and interpretability in downstream tasks. |
The Program Testing Ability of Large Language Models for Code (2024.emnlp-industry)
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| Challenge: | Recent development of large language models (LLMs) for code shows promise in achieving code intelligence. |
| Approach: | They explore the ability of large language models to generate automated test cases . they show +11.77% and +4.22% higher code pass rates on HumanEval+ . |
| Outcome: | The proposed models show higher pass rates on humanEval+ compared with the current state-of-the-art models. |