The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. However, these prompts often tend to be brief and vague, thereby significantly limiting the full potential of LLMs. Moreover, harmful prompts can be meticulously crafted and manipulated by adversaries to jailbreak LLMs, inducing them to produce potentially toxic content. To enhance the capabilities of LLMs while maintaining strong robustness against harmful jailbreak inputs, this study proposes a transferable and pluggable framework that refines user prompts before they are input into LLMs. This strategy improves the quality of the queries, empowering LLMs to generate more truthful, benign and useful responses. Specifically, a lightweight query refinement model is introduced and trained using a specially designed reinforcement learning approach that incorporates multiple objectives to enhance particular capabilities of LLMs. Extensive experiments demonstrate that the refinement model not only improves the quality of responses but also strengthens their robustness against jailbreak attacks. Code is available at: https://github.com/Huangzisu/query-refinement .
Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement
University of Manchester（曼彻斯特大学）；Idiap Research Institute（Idiap研究所）；National Biomarker Centre, CRUK-MI, University of Manchester（曼彻斯特大学英国癌症研究中心曼彻斯特研究所国家生物标志物中心）
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Wuhan University of Science and Technology（武汉科技大学）；National University of Singapore（新加坡国立大学）；Northeastern University at Qinhuangdao（东北大学秦皇岛分校）；BOSS Zhipin（BOSS直聘）
Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Huawei Noah’s Ark Lab（华为诺亚实验室）；College of AI, Tsinghua University（清华大学人工智能学院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States
King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）；Centre for AI, Data Science & Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca（人工智能、数据科学与人工智能研究中心，生物制药研发，阿斯利康）；The Chinese University of Hong Kong（香港中文大学）；MoE Lab, CUHK（CUHK MoE实验室）
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学公安学院人工智能学院）；Sichuan University（四川大学）；School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Tsinghua University（清华大学）；School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education（教育部下一代智能搜索与推荐工程研究中心）
A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
School of Computer and Communication Engineering, University of Science and Technology Beijing（计算机与通信工程学院，北京科技大学）；Blekinge Institute of Technology（布莱金厄学院）
PankRAG: Enhancing Graph Retrieval via Globally Aware Query Resolution and Dependency-Aware Reranking Mechanism
College of Computer Science, Beijing university of technology, Beijing, China（北京理工大学计算机科学学院）；Department of Computer Science and Engineering, Bologna University, Bologna, Italy（博洛尼亚大学计算机科学与工程系）
$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
Technical University of Darmstadt（达姆施塔特工业大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；University of Pennsylvania（宾夕法尼亚大学）；Tencent AI Lab（腾讯AI实验室）
