Papers by Chenbin Chenbin
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)
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| Challenge: | Existing models for response selection do not perform well when there are many candidate responses. |
| Approach: | They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response . |
| Outcome: | The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks. |
OASIS: Order-Augmented Strategy for Improved Code Search (2025.acl-long)
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Gao Zuchen, Zizheng Zhan, Xianming Li, Erxin Yu, Haotian Zhang, Chenbin Chenbin, Yuqun Zhang, Jing Li
| Challenge: | Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications. |
| Approach: | They propose an order-augmented strategy for improved code search that leverages order-based similarity labels to capture subtle differences in similarity among negative pairs. |
| Outcome: | The proposed model outperforms state-of-the-art models focusing on major positive-negative differences. |
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)
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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong
| Challenge: | Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. |
| Approach: | They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process. |
| Outcome: | Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models. |