Papers by Guoqiang Li
Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories (2022.coling-1)
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| Challenge: | Stack Overflow and GitHub are open source communities that are gaining popularity . developers need to raise programming questions in coding forums and navigate to GitHub repositories . |
| Approach: | They propose a questionrepository matching task that bridges the gap between repositories and real-world coding questions. |
| Outcome: | The proposed model outperforms state-of-the-art methods on coding questions and repositories . it can find suitable coding repositoriels and bridge the gap between them . |
JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework (2026.acl-demo)
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| Challenge: | Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets. |
| Approach: | They propose to use a Chinese ICD coding dataset and a multi-agent framework to reformulate ICD as a joint disease-procedure coding task. |
| Outcome: | The proposed system outperforms state-of-the-art methods on real-world Chinese ICD coding datasets and 1.7B-parameter models. |
CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System (2025.acl-long)
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Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, Nenghai Yu
| Challenge: | CompileAgent is the first LLM-based agent framework dedicated to repo-level compilation. |
| Approach: | They propose a LLM-based agent framework dedicated to repo-level compilation. |
| Outcome: | The proposed method significantly improves compilation success rate, ranging from 10% to 71%. |
DCE-LLM: Dead Code Elimination with Large Language Models (2025.naacl-long)
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| Challenge: | Dead code can obscure logical errors and be exploited for obfuscation in malware. |
| Approach: | They propose a framework for automated dead code elimination using a codeBERT model with an attribution-based line selector. |
| Outcome: | Experimental results show that DCE-LLM outperforms existing tools for dead code elimination . dead code can obscure logical errors and be exploited for obfuscation in malware . |
Co-Eval: Augmenting LLM-based Evaluation with Machine Metrics (2025.emnlp-main)
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| Challenge: | Existing LLMs suffer from biases and misalignment due to limited functional understanding and knowledge gaps. |
| Approach: | They introduce a framework that leverages a criteria planner model and optimized machine metrics to enhance the scalability and fairness of LLM-based evaluation. |
| Outcome: | The proposed framework reduces biases and improves alignment with human preferences, with gains of up to 0.324 in Spearman correlation. |
Intra-Correlation Encoding for Chinese Sentence Intention Matching (2020.coling-main)
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| Challenge: | Existing methods to improve sentence intention matching for Chinese text are limited due to the particularity of the text. |
| Approach: | They propose a method that combines character-granularity and word-granulularity features to perform sentence intention matching. |
| Outcome: | The proposed method can capture sentence feature information from multiple perspectives and correlation information between different levels of sentences. |
Think Earlier, Not Longer: Prompt Optimization via Reducing Unhealthy Exploration (2026.findings-acl)
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| Challenge: | Existing approaches to improve reasoning performance ignore the presence of unhealthy exploration that increases token usage without contributing to effective problem-solving. |
| Approach: | They propose an entropy-dynamics-aware prompt optimization framework that trains a lightweight optimizer to generate concise clarifications. |
| Outcome: | The proposed framework reduces ambiguity-induced early-stage uncertainty while preserving the model's reasoning capabilities. |