Papers by Shun Zhang
CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion (2025.emnlp-main)
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| Challenge: | Recent advances in code large language models have produced repository-level code completion methods that automatically predict the unfinished code based on the broader information from the repository. |
| Approach: | They propose a framework to identify relevant knowledge for retrieval-augmented repository-level code completion. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art methods on ReccEval and CCEval. |
New Intent Discovery with Attracting and Dispersing Prototype (2024.lrec-main)
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| Challenge: | Existing methods for detecting new intents with labeled data are not cluster-friendly . a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype . |
| Approach: | They propose a robust and adaptive prototypical learning framework for globally distinct decision boundaries for both known and new intent categories. |
| Outcome: | The proposed method improves on CLINC, BANKING, and StackOverflow benchmarks on three challenging benchmarks. |
C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)
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| Challenge: | Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples. |
| Approach: | They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. |
| Outcome: | The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks. |
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language (2024.emnlp-main)
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| Challenge: | Existing evaluation methods focus on fluency and factual reliability, while neglecting figurative quality. |
| Approach: | They propose a set of human evaluation metrics focused on the translation of figurative language and a parallel metaphor corpus generated by post-editing. |
| Outcome: | The proposed evaluation protocol estimates four aspects of MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality. |
KMatrix: A Flexible Heterogeneous Knowledge Enhancement Toolkit for Large Language Model (2024.emnlp-demo)
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| Challenge: | Existing Knowledge-Enhanced Large Language Models (K-LLMs) toolkits focus on free-textual knowledge and lack robust datasets, models, and user-friendly experience. |
| Approach: | They propose a flexible heterogeneous knowledge enhancement toolkit to enhance Large Language Models (LLMs) using external knowledge. |
| Outcome: | KMatrix: a flexible heterogeneous knowledge enhancement toolkit for LLMs includes verbalizing-retrieval and parsing-query methods. |
KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)
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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu
| Challenge: | Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) . |
| Approach: | They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs). |
| Outcome: | The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement. |
Towards Multi-System Log Anomaly Detection (2025.acl-industry)
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| Challenge: | Existing models require dataset-specific training, causing costly procedures and performance bottlenecks. |
| Approach: | They propose a log anomaly detection model with semantic relational reasoning that extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors. |
| Outcome: | The proposed model extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors. |
Towards Real-world Scenario: Imbalanced New Intent Discovery (2024.acl-long)
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| Challenge: | Existing studies focus on detecting known and previously undefined categories of user intent . skewed and long-tailed distributions often encountered in open-world scenarios . |
| Approach: | They propose to use imbalanced new intent discovery task to identify familiar and novel intent categories within long-tailed distributions. |
| Outcome: | The proposed model outperforms the existing benchmark on three datasets to simulate the real-world long-tail distributions. |
CAIR: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for multimodal emotion reasoning produce fluent but superficial explanations that lack authentic logical derivation. |
| Approach: | They propose a framework that treats rationales as causal mediators between raw perceptual signals and emotional semantics and an adaptive optimization mechanism to balance perception and reasoning across varying cognitive loads. |
| Outcome: | The proposed framework outperforms specialized SFT models by 14.4% while enhancing rationale faithfulness. |