Jiapu Wang
2026
Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning
Wenjin Liu | Haoran Luo | Xueyuan Lin | Haoming Liu | Tiesunlong Shen | Jiapu Wang | Rui Mao | Erik Cambria
Findings of the Association for Computational Linguistics: ACL 2026
Wenjin Liu | Haoran Luo | Xueyuan Lin | Haoming Liu | Tiesunlong Shen | Jiapu Wang | Rui Mao | Erik Cambria
Findings of the Association for Computational Linguistics: ACL 2026
Recently, various excellent and powerful large language models (LLMs) have been utilized to solve a wide range of human problems. However, when faced with complex problems, most users are often unable to provide accurate and effective prompts to interact with LLMs, thus limiting their performance. To address this challenge, we propose Prompt-R1, an end-to-end reinforcement learning framework that utilizes a small-scale LLM (as agent) to collaborate with large-scale LLMs (as environment), replacing users to interact better. This collaboration is presented as a multi-turn interaction, where the small-scale LLM thinks and generates prompts, and the large-scale LLM performs complex reasoning. A double-constrained reward is designed to optimize correctness and quality of generation. Prompt-R1 provides a plug-and-play framework that supports both inference and training with various large-scale LLMs. Experimental results on twelve datasets show that Prompt-R1 significantly outperforms baseline LLMs across various tasks.Our code is available at https://github.com/QwenQKing/Prompt-R1.
LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence
Wenjin Liu | Haoran Luo | Xin Feng | Xiang Ji | Lijuan Zhou | Rui Mao | Jiapu Wang | Shirui Pan | Erik Cambria
Findings of the Association for Computational Linguistics: ACL 2026
Wenjin Liu | Haoran Luo | Xin Feng | Xiang Ji | Lijuan Zhou | Rui Mao | Jiapu Wang | Shirui Pan | Erik Cambria
Findings of the Association for Computational Linguistics: ACL 2026
Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making, simulating the expertise of legal experts across domains. However, existing benchmarks are result-oriented and fail to systematically evaluate the legal intelligence of large language models (LLMs), hindering the development of legal GI. To address this, we propose LexGenius, an expert-level Chinese legal benchmark for evaluating legal GI in LLMs. It follows a Dimension-Task-Ability framework, covering seven dimensions, eleven tasks, and twenty abilities. We use the recent legal cases and exam questions to create multiple-choice questions with a combination of manual and LLM reviews to reduce data leakage risks, ensuring accuracy and reliability through multiple rounds of checks. We evaluate 12 state-of-the-art LLMs using LexGenius and conduct an in-depth analysis. We find significant disparities across legal intelligence abilities for LLMs, with even the best LLMs lagging behind human legal professionals. We believe LexGenius can assess the legal intelligence abilities of LLMs and enhance legal GI development.Our project is available at https://github.com/QwenQKing/LexGenius.