Papers by Yanjie Fu
Weaver: Interweaving SQL and LLM for Table Reasoning (2025.emnlp-main)
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| Challenge: | Existing approaches that combine SQL and LLM rely on rigid workflows . Tables play a critical role across various domains such as finance, healthcare and scientific research . |
| Approach: | Weaver is a modular pipeline that integrates SQL and LLM for table-based question answering. |
| Outcome: | Weaver outperforms state-of-the-art methods on four Table QA datasets. |
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer (2026.findings-eacl)
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Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya, Xinyue Ye, Dongjie Wang, Yanjie Fu, Kunpeng Liu
| Challenge: | Existing studies show that large language models have strong reasoning capabilities through chain-structured methods. |
| Approach: | They propose a framework for navigating and expanding thought structures to overcome blind spots in LLM reasoning. |
| Outcome: | The proposed framework overcomes blind spots in large language models by expanding thought structures . the proposed framework improves accuracy of the final answer and intermediate reasoning steps . |
Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement (2026.findings-eacl)
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Wangyang Ying, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Recent advances in large language models (LLMs) show potential for graph extraction, but often yield ill-formed structures or misinterpret logical constructs such as gateways. |
| Approach: | They propose a framework that treats procedural graph extraction as a multi-round reasoning process with structural and logical refinement agents. |
| Outcome: | The proposed framework achieves significant improvements in structural correctness and logical consistency over strong baselines. |
Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations (2026.findings-acl)
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| Challenge: | Existing benchmarks measure ToM capability improvement through story-reading, multiple-choice questions from a third-person perspective, while ignoring the first-person, dynamic nature of human-AI interactions. |
| Approach: | They propose a new paradigm of interactive ToM evaluation with both perspective and metric shifts. |
| Outcome: | The proposed approach improves the performance of four representative LLM enhancement techniques using real-world datasets and a user study. |
MixLLM: Dynamic Routing in Mixed Large Language Models (2025.naacl-long)
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Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Large Language Models (LLMs) exhibit potential artificial generic intelligence, however, their usage is costly with high response latency. |
| Approach: | They develop a dynamic contextual-bandit-based routing system for query-LLM assignment that leverages query tags to enhance query embeddings. |
| Outcome: | The proposed model maximizes response quality and minimizes cost and latency. |
ISACL: Internal State Analyzer for Copyrighted Training Data Leakage (2025.findings-emnlp)
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| Challenge: | Traditional methods address leaks only after content is generated, which can lead to the exposure of sensitive information. |
| Approach: | They propose a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. |
| Outcome: | The proposed framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. |