Papers by Yuanchun Li
Benchmarking LLM’s Capability in Reasoning over Conflicting Web References (2026.acl-long)
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| Challenge: | Large language models (LLMs) integrated with retrieval-augmented generation (RAG) are a dominant framework for building intelligent assistants. |
| Approach: | They propose a benchmark to evaluate LLMs' reasoning capability over real-world conflicting documents retrieved from the web. |
| Outcome: | The proposed benchmark evaluates LLMs' reasoning capability over real-world conflicting documents retrieved from the web. |
RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)
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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale. |
| Approach: | They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers. |
| Outcome: | The proposed model is based on human-verified QA pairs and contains 15K questions. |
SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget (2024.acl-long)
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| Challenge: | Mixture of experts (MoE) is a popular technique to improve capacity of Large Language Models (LLMs) but memory-constrained devices are a major concern in edge AI training and serving. |
| Approach: | They propose a framework for efficient serving of MoE-based large language models with tunable memory budgets. |
| Outcome: | Experiments show that SwapMoE can reduce memory consumption while maintaining reasonable accuracy. |
An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)
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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu
| Challenge: | Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints. |
| Approach: | They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking . |
| Outcome: | The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints. |
Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision (2026.acl-long)
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Ge Chang, Jinbo Su, Jiacheng Liu, Pengfei Yang, Yuhao Shang, Huiwen Zheng, Hongli Ma, Yan Liang, Yuanchun Li, Yunxin Liu
| Challenge: | Existing methods for integrating textual graphs with LLMs are limited by symbolic inference and high annotation costs. |
| Approach: | They propose a textual graph reasoning framework that integrates textual diagrams with large language models. |
| Outcome: | The proposed approach achieves 15.6% accuracy and 17.2% in F1 score on three common datasets. |