Papers by Wenhan Wu
AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation (2026.acl-long)
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| Challenge: | Existing methods for aggregating large-form outputs overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition. |
| Approach: | They propose a UQ framework that uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing computation costs. |
| Outcome: | Experiments on BIO and LongFact show that the proposed framework reduces inference time by 60% compared to full atomic decomposition. |
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification (D19-1)
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| Challenge: | Existing models for multi-label classification ignore complexity and dependencies among labels . Experimental results show that our method can obtain more accurate multi-lab classification results. |
| Approach: | They propose a meta-learning method to capture complex label dependencies . they use a Meta-learner to jointly learn the training policies and prediction policies for different labels. |
| Outcome: | The proposed method can capture complex label dependencies on fine-grained entity typing and text classification tasks. |
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 . |
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. |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)
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| Challenge: | Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations. |
| Approach: | They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities. |
| Outcome: | The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types. |
T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)
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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li
| Challenge: | Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications. |
| Approach: | They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning. |
| Outcome: | The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation. |
TWEETQA: A Social Media Focused Question Answering Dataset (P19-1)
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Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
| Challenge: | Social media is becoming an important realtime information source, especially during natural disasters and emergencies. |
| Approach: | They present a large-scale dataset for question answering over social media data . they gather tweets used by journalists and ask human annotators to write questions upon them . |
| Outcome: | The proposed dataset shows that neural models that perform well on formal texts are limited in their performance . the proposed model is still lagging behind human performance with a large margin . |