Papers by Dexun Li
Planning with Multi-Constraints via Collaborative Language Agents (2025.coling-main)
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| Challenge: | Recent advances in neural language models have sparked a new surge of intelligent agent research. |
| Approach: | They propose a method for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. |
| Outcome: | The proposed method achieves an average success rate of 42.68% on two constraint-intensive benchmarks, TravelPlanner and API-Bank. |
MMDocIR: Benchmarking Multimodal Retrieval for Long Documents (2025.emnlp-main)
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| Challenge: | Existing benchmarks for multimodal document retrieval are lacking for evaluating performance of systems. |
| Approach: | They propose a benchmark that evaluates page-level and layout-level retrieval tasks . they use a rich dataset featuring 1,685 questions annotated by experts . |
| Outcome: | The proposed benchmark outperforms existing benchmarks in page-level and layout-level retrieval tasks. |
ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)
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Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu
| Challenge: | Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead. |
| Approach: | ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. |
| Outcome: | ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. |
RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery (2025.findings-acl)
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| Challenge: | Existing methods for knowledge-intensive long texts struggle with issues like hallucinations, topic incoherence, and significant latency. |
| Approach: | They propose a retrieval-augmented long text generation framework with writing P**lanning and I**nformation to address these challenges. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a freshWiki-2024 dataset. |
Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger (2025.acl-long)
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Wenjun Li, Dexun Li, Kuicai Dong, Cong Zhang, Hao Zhang, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Liu
| Challenge: | Existing research expands the tool arrays of large language models (LLMs), but the necessity of using these tools is often overlooked, leading to indiscriminate tool invocation. |
| Approach: | They propose a meta-cognition proxy proxy for LLMs self-assessment of their capabilities, reflecting the model’s awareness of its own limitations. |
| Outcome: | The proposed strategy is fine-tuned-free and costs minimal. |