Papers by Wenjin Wang
Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026.findings-acl)
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Wenjin Liu, Haoran Luo, Xueyuan Lin, Haoming Liu, Tiesunlong Shen, Jiapu Wang, Rui Mao, Erik Cambria
| Challenge: | Existing large language models are limited in understanding, reasoning, calculation, and generation, limiting their performance in complex reasoning and dynamic tasks. |
| Approach: | They propose a plug-and-play framework that integrates a small-scale LLM (as agent) with large-scale large-level LLMs (a as environment) they propose generating prompts that are used to interact with LLM, and a double constraint reward that optimizes correctness and quality of generation. |
| Outcome: | The proposed framework significantly outperforms baseline large-scale large-language models across various tasks. |
PURE: Aligning LLM via Pluggable Query Reformulation for Enhanced Helpfulness (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) depend on vast amounts of text data sourced from the Internet for their training. |
| Approach: | They propose a new alignment paradigm that reformulates risky queries into highly relevant yet harmless ones before feeding them into LLMs. |
| Outcome: | The proposed approach eliminates the high costs of training base LLMs and achieves a promising balance of harmlessness and helpfulness. |
Tool learning via Inference-time Scaling and Cycle Verifier (2025.findings-acl)
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| Challenge: | In inference-time scaling, Chain-of-Thought (CoT) data is scarce or even unavailable. |
| Approach: | They propose a method which establishes an inference cycle to synthesize user queries and CoT data. |
| Outcome: | The proposed method achieves a 75.4% pass rate and a 79.6% win rate using small models in StableToolBench. |
Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) evolve into agentic systems capable of autonomous tool invocation and complex reasoning. |
| Approach: | They propose a trajectory-level preference benchmark to evaluate judges' ability to distinguish preferred versus distractor agent trajectories in tool-integrated environments. |
| Outcome: | The proposed benchmark evaluates how well judges distinguish preferred versus distractor agent trajectories in complex tool-using scenarios. |
LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)
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Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria
| Challenge: | Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs). |
| Approach: | They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions . |
| Outcome: | The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals. |
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models (2024.acl-long)
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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Wei Ye, Jindong Wang, Xing Xie, Yue Zhang, Shikun Zhang
| Challenge: | Existing methods to detect contaminated texts focus on quantifying contamination status instead of accurately gauging model performance. |
| Approach: | They propose a Knowledge-grounded Interactive Evaluation framework which incorporates an LLM-powered “interactor” role for the first time to accomplish a dynamic contamination-resilient evaluation. |
| Outcome: | The proposed framework is based on a question in a standard LLM benchmark and can be used to evaluate models in real-world conversations. |
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)
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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness . |
| Approach: | They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image. |
| Outcome: | The proposed model outperforms existing models on key downstream tasks. |
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models (2024.emnlp-demo)
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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing with impressive performance across various tasks. |
| Approach: | They propose a framework for automated evaluations of large language models . they open-source their code at https://github.com/WisdomShell/FreeEval . |
| Outcome: | The framework is open-source and can be used to develop and validate new evaluation methods. |