Papers by Wenhan Chao
Characterizing and Verifying Scientific Claims: Qualitative Causal Structure is All You Need (2023.emnlp-main)
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| Challenge: | a scientific claim verification requires thorough examination and assessment to ascertain its validity . attention architectures and pre-trained language models fail to establish a comprehensive chain of causal inference . |
| Approach: | They propose a qualitative causal structure-based graph neural network model to facilitate causal reasoning across relevant causally-potent factors. |
| Outcome: | The proposed model outperforms state-of-the-art models by incorporating semantic features . the proposed model is based on a qualitative causal structure . |
Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description (2020.acl-main)
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| Challenge: | Existing studies on complex criminal cases with multiple defendants only focus on the simple cases with one defendant. |
| Approach: | They propose to model the defendants with behavioral semantic information and statistical characteristics, then learning the importances of defendants within a learning-to-rank framework. |
| Outcome: | The proposed model can model the defendants’ impacts in a complex case. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)
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| Challenge: | Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way. |
| Approach: | They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. |
| Outcome: | The proposed method can be used to generate a state-of-the-art on a small dataset. |
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions (N18-1)
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| Challenge: | Existing work on court view generation from fact descriptions has improved the working efficiency of legal assistant systems. |
| Approach: | They propose to decode court views conditioned on encoded charge labels from the fact description in a criminal case to improve interpretability of charge prediction systems. |
| Outcome: | The proposed model can generate court views conditioned on encoded charge labels. |