Papers by Wenbo Zhao
Mitigating Bias for Question Answering Models by Tracking Bias Influence (2024.naacl-long)
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Mingyu Ma, Jiun-Yu Kao, Arpit Gupta, Yu-Hsiang Lin, Wenbo Zhao, Tagyoung Chung, Wei Wang, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing literature observes bias in question answering (QA) models, but there is no method to mitigate it. |
| Approach: | They propose an approach to mitigate the bias of question answering models by observing the influence of a query instance on another instance. |
| Outcome: | The proposed method reduces bias level in all 9 bias categories while maintaining comparable QA accuracy. |
PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing (2025.coling-industry)
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| Challenge: | Existing code large language models focus on generating correct code, but struggle with bug repair. |
| Approach: | They propose a set of methods to enhance LLM’s SQL bug-fixing abilities by combining a data set construction and a supervised bug-fixed learning approach. |
| Outcome: | The proposed methods exceed current best performing model which size is much larger. |
Rumor Detection on Social Media with Crowd Intelligence and ChatGPT-Assisted Networks (2023.emnlp-main)
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| Challenge: | Existing research on rumor detection challenges the expressive power of text encoding sequences, and insufficient mining of semantic structural information. |
| Approach: | They propose a Crowd Intelligence-based semantic feature learning module to capture textual content’s sequential and hierarchical features and a knowledge-based structural mining module that leverages ChatGPT for knowledge enhancement. |
| Outcome: | The proposed system achieves performance improvement in rumor detection tasks validating the effectiveness and rationality of using large language models as auxiliary tools. |
ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants (2026.acl-long)
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Pei Wang, Yanan Wu, Xiaoshuai Song, Weixun Wang, Gengru Chen, Zhongwen Li, Kezhong Yan, Qi Liu, Ken Deng, Shuaibing Zhao, Shaopan Xiong, Xuepeng Liu, Xuefeng Chen, Wanxi Deng, Wenbo Su, Bo Zheng
| Challenge: | Existing studies on large language model-based agents focus on evaluation benchmarks without training support. |
| Approach: | They propose a large-scale Chinese shopping simulation environment that uses large language models to train agents. |
| Outcome: | The proposed model performs poorly in a large-scale and challenging shopping environment in China. |
Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)
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| Challenge: | Knowledge graph based simple question answering is a major area of research in question answering. |
| Approach: | They propose a framework to describe and analyze existing knowledge graph based simple question answering approaches. |
| Outcome: | The proposed model achieves a state-of-the-art (85.44% accuracy) on the SimpleQuestions dataset. |
Read As Human: Compressing Context via Parallelizable Close Reading and Skimming (2026.acl-long)
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Jiwei Tang, Shilei Liu, Zhicheng Zhang, Qingsong Lv, Runsong Zhao, Tingwei Lu, Langming Liu, Haibin Chen, Yujin Yuan, Hai-Tao Zheng, Wenbo Su, Bo Zheng
| Challenge: | Existing task-aware methods require loading the entire input sequence at once for compression, which suffer from computational inefficiency. |
| Approach: | They propose a framework that adopts an adaptive hybrid reading strategy to reduce computational inefficiency and redundant information in long-context scenarios. |
| Outcome: | Experiments show that RAM outperforms baselines on multiple question answering and summarization benchmarks while delivering up to a 12x speedup on long inputs. |
See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)
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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng
| Challenge: | Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence. |
| Approach: | They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction. |
| Outcome: | The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
AGENTVIGIL: Automatic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents (2025.findings-emnlp)
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Zhun Wang, Vincent Siu, Zhe Ye, Tianneng Shi, Yuzhou Nie, Xuandong Zhao, Chenguang Wang, Wenbo Guo, Dawn Song
| Challenge: | AGENTVIGIL is a black-box optimization framework to exploit indirect prompt injection vulnerabilities . indirect prompts compromise the core of LLM agents by manipulating contextual information rather than direct user prompts. |
| Approach: | They propose a black-box optimization framework to exploit indirect prompt injection vulnerabilities . they use a Monte Carlo tree-based algorithm to iteratively refine inputs . |
| Outcome: | The proposed framework achieves 71% and 70% success rates against two public benchmarks . |
RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models (2026.findings-acl)
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| Challenge: | Experiments show that reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs) but requires a key prerequisite: the model must already be able to generate high-utility reasoning paths with non-negligible probability. |
| Approach: | They propose a framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning. |
| Outcome: | Experiments on 11 benchmarks and 3 models show that RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning. |
CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling (2026.acl-long)
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Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, JingBo Zhu, Wenbo Su, Bo Zheng
| Challenge: | a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity is a major barrier to long-context processing. |
| Approach: | They propose a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity. |
| Outcome: | The proposed architecture can handle arbitrarily long sequences with constant memory usage and linear time complexity. |
FinMath: Injecting a Tree-structured Solver for Question Answering over Financial Reports (2022.lrec-1)
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| Challenge: | Existing models for answering complex questions require multiple-step numerical reasoning. |
| Approach: | They propose a framework that injects a tree-structured neural model into a model to perform multi-step numerical reasoning. |
| Outcome: | The proposed framework improves the previous best model by 8.5% absolute for Exact Match (EM) score and 6.1% absolute for numeracy-focused F1 score. |
“I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in Chinese (2022.findings-emnlp)
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| Challenge: | a recent study has focused on how to recognize punchlines from dialogues, but has neglected character information. |
| Approach: | They propose a character-fusion conversational humor recognition model that uses character information to recognize punchlines from dialogue. |
| Outcome: | The proposed model improves performance on Chinese sitcoms corpus and punchline identification. |
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)
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Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
| Challenge: | Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data. |
| Approach: | They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data. |
| Outcome: | The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models. |
Incorporating Object-Level Visual Context for Multimodal Fine-Grained Entity Typing (2023.findings-emnlp)
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| Challenge: | Experimental results show that fine-grained entity typing is superior to text-based methods. |
| Approach: | They propose a task called fine-grained entity typing to classify entities . they propose combining textual and visual contexts to capture fine-granular semantic information . |
| Outcome: | The proposed approach achieves superior classification performance compared to previous text-based approaches. |
M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)
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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng
| Challenge: | Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities . |
| Approach: | They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models. |
| Outcome: | The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree. |