Papers by Xuanhua Shi
Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning (2024.findings-acl)
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| Challenge: | Existing CodePre-trained models struggle to generalize due to superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities. |
| Approach: | They propose a framework that integrates multi-task learning with Large Language Models to effectively mine deep-seated vulnerability features. |
| Outcome: | The proposed framework surpasses seven state-of-the-art models in effectiveness, generalization, and robustness. |
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)
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| Challenge: | Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation. |
| Approach: | They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them. |
| Outcome: | The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches. |
code-transformed: The Influence of Large Language Models on Code (2026.findings-eacl)
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| Challenge: | Using Large Language Models, code generation capabilities have transformed programming practices. |
| Approach: | They analyze 20,000 GitHub repositories linked to arXiv papers published between 2020 and 2025 . they identify measurable trends in the evolution of coding style that align with LLM-generated code . |
| Outcome: | The proposed study examines 20,000 GitHub repositories linked to arXiv papers . it finds that LLMs influence code style, and that they can be observed in real-world code . |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
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| Challenge: | Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability. |
| Approach: | They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference. |
| Outcome: | The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability. |
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback (2024.findings-acl)
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Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Hai Jin, Xuanhua Shi
| Challenge: | Large Language Models (LLMs) generate code for given contexts, such as incomplete code, class, data structure, or project-specific information. |
| Approach: | They propose a compiler feedback-based code generation approach that leverages static analysis to identify mismatches between the generated code and the project's context. |
| Outcome: | The proposed model outperforms retrieval-based code generation baselines and significantly outperfies the existing large language models. |
CoSToM: Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Large language models lack intrinsic cognition and cannot generalize to complex task-specific scenarios. |
| Approach: | They propose a framework that transitions from mechanistic interpretation to active intervention to map internal distributions of ToM features and implement it via targeted activation steering within ToM-critical layers. |
| Outcome: | The proposed framework significantly enhances human-like social reasoning capabilities and dialogue quality. |