Papers by Wenbo Yu
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. |
MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG (2025.findings-naacl)
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| Challenge: | Existing approaches to retrieve entity information are limited by document level retrieval and intermingled storage of information from different entities. |
| Approach: | They propose a framework that enhances entity-specific query handling . MES-RAG introduces proactive security measures that ensure system integrity . |
| Outcome: | Experimental results show that MES-RAG improves accuracy and recall . the framework can be integrated into existing RAG architectures . |
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. |
OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)
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Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang
| Challenge: | evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user. |
| Approach: | They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. |
| Outcome: | The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user. |
AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)
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| Challenge: | Existing methods for generating complex instructions are resource-intensive and lack diversity. |
| Approach: | They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance. |
| Outcome: | The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods. |
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have advanced machine translation (MT) a meta-evaluation dataset focused on non-literal translations is lacking . experimental results show the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge. |
| Approach: | They propose a meta-evaluation framework that leverages sub-agents to evaluate machine translation metrics. |
| Outcome: | The proposed framework improves on the knowledge cutoff and score inconsistency problem. |
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)
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| Challenge: | End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity. |
| Approach: | They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends. |
| Outcome: | The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. |
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. |