Papers by Lecheng Zheng
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
Multi-label Sequential Sentence Classification via Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing approaches to sequential sentence classification are constrained by model size, sequence length, and single-label setting. |
| Approach: | They propose a large language model-based framework for both single- and multi-label SSC tasks that generate SSC labels through designed prompts. |
| Outcome: | The proposed framework enhances task understanding by incorporating demonstrations and a query to describe the prediction target. |
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? (2025.acl-long)
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| Challenge: | Graph Neural Networks (GNNs) with CLIP pipeline are difficult because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and conceptual gaps between domains. |
| Approach: | They propose a multi-modal prompt learning paradigm to adapt pre-trained GNNs to downstream tasks with weak text supervision. |
| Outcome: | The proposed model can generalize graphs to unseen classes with weak text supervision. |
BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels (2026.acl-short)
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| Challenge: | Existing biomedical generative retrievers lack domain semantics and hierarchical relationships among biomedically related texts. |
| Approach: | They propose a biomedical retrieval model with hierarchical multi-label contrastive learning that leverages hierarchic MeSH annotations to provide structured supervision for multi-labor contrastive training. |
| Outcome: | The proposed models achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks while remaining computationally efficient for deployment. |