Papers by Zichen Chen
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge. |
| Approach: | They propose a dataset that includes 24204 instances where each instance interprets the LLM’s reasoning behavior using knowledge graphs and graph attention networks (GAT). |
| Outcome: | The proposed explanation framework reduces hallucinations and improves grounded explanation generation in large language models. |
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)
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| Challenge: | Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task. |
| Approach: | They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs. |
| Outcome: | The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs. |
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization (2026.acl-long)
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Linfeng Du, Ye Yuan, Zichen Zhao, Fuyuan Lyu, Emiliano Penaloza, Xiuying Chen, Zipeng Sun, Jikun Kang, Laurent Charlin, Xue Liu, Haolun Wu
| Challenge: | Existing approaches for personalizing large language models require modifying parameters. |
| Approach: | They propose a lightweight approach to personalizing large language models via retrieval augmentation . relevance serves as an unreliable proxy for utility, they argue . |
| Outcome: | The proposed framework outperforms strong heuristic and retrieval-augmented baselines on nine personalization tasks. |
Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. |
| Approach: | They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence. |
| Outcome: | The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage. |
SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)
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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni
| Challenge: | Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging . |
| Approach: | They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks. |
| Outcome: | The proposed framework bridges the domain gap between LLMs and recommendation tasks. |
GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated significant capabilities in processing and understanding text data. |
| Approach: | They propose a structure-based instruction-based method to enhance LLM performance on complex graph tasks. |
| Outcome: | The proposed framework outperforms open-source models on graph problem-solving, but the gap is narrowing. |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)
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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu
| Challenge: | Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. |
| Approach: | They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks. |
| Outcome: | The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity. |
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems (2026.acl-long)
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| Challenge: | Automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption. |
| Approach: | They propose a plug-and-play compression framework for graph-structured multi-agent workflows . they estimate the importance score of each agent and remove redundant agents . |
| Outcome: | Experiments show that AgentSlimming reduces average token cost by 78.9% with negligible performance degradation. |
From Tasks to Teams: A Risk-First Evaluation Framework for Multi-Agent LLM Systems in Finance (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on task specific metrics such as accuracy, F1 score, or ROUGE. |
| Approach: | They propose a multi-agent, safety-aware evaluation agent that audits large language models without fine-tuning. |
| Outcome: | M-SAEA identifies unsafe trajectories with minimal false positives and reveals latent risks that are not addressed by standard metrics. |
Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)
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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin
| Challenge: | Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. |
| Approach: | They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations. |
| Outcome: | The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images. |