Large language models (LLMs) have demonstrated remarkable and steadily improving performance across a wide range of tasks. However, LLM performance may be highly sensitive to prompt variations especially in scenarios with limited openness or strict output formatting requirements, indicating insufficient robustness. In real-world applications, user prompts provided to LLMs often contain imperfections, which may undermine the quality of the model's responses. To address this issue, previous work has primarily focused on preprocessing prompts, employing external tools or even LLMs to refine prompt formulations in advance. However, these approaches overlook the intrinsic robustness of LLMs, and their reliance on external components introduces additional computational overhead and uncertainty. In this work, we propose a Contrastive Learning-based Inverse Direct Preference Optimization (CoIPO) method that minimizes the discrepancy between the label-aligned logits produced by the model under a clean prompt and its noisy counterpart, and conduct a detailed analysis using mutual information theory. We augment the FLAN dataset by constructing paired prompts, each consisting of a clean prompt and its corresponding noisy version for training. Additionally, to evaluate the effectiveness, we develop NoisyPromptBench, a benchmark enhanced and derived from the existing PromptBench. Experimental results conducted on NoisyPromptBench demonstrate that our proposed method achieves a significant improvement in average accuracy over the current state-of-the-art approaches. The source code of CoIPO, pair-wise FLAN datasets, and NoisyPromptBench have already been released on https://github.com/vegetable-yx/CoIPO.
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Nanyang Technological University（南洋理工大学）；University of New South Wales（新南威尔士大学）；Virginia Tech（弗吉尼亚理工大学）；Huazhong University of Science and Technology（华中科技大学）
PGCLODA: Prompt-Guided Graph Contrastive Learning for Oligopeptide-Infectious Disease Association Prediction
Institute of Physical Science and Information Technology（物理科学与信息技术研究所）；Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education（教育部智能计算与信号处理重点实验室）；School of Artificial Intelligence（人工智能学院）
LLM-as-a-Reviewer: Benchmarking Their Ability, Divergence, and Prompt Injection Resistance as Paper Reviewers
University of South Florida（佛罗里达南大学）；Missouri University of Science and Technology（密苏里科技大学）；University of Alabama（阿拉巴马大学）；Florida International University（佛罗里达国际大学）；University of Cincinnati（辛辛那提大学）；George Mason University（乔治·梅森大学）
AvalonBench: Evaluating LLMs Playing the Game of Avalon
University of California, Berkeley（加州大学伯克利分校）；California Institute of Technology（加州理工学院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Shenzhen University（深圳大学）
Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；National University of Singapore（新加坡国立大学）；Inner Mongolia Research Institute, Shanghai Jiao Tong University（上海交通大学内蒙古研究院）
Friend or Foe? Language as an ideological switch in open-weight LLMs under Russian disinformation stress
Institute of Culture Studies, University of Silesia in Katowice（文化研究学院，卡托维察大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；National Aviation University（国家航空大学）
SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue
Monash University（莫纳什大学）；University of Liverpool（利物浦大学）；University of Edinburgh（爱丁堡大学）；Federation University（联邦大学）；Orygen, The University of Melbourne（墨尔本大学下属Orygen机构）
Yet another algorithmic bias: A Discursive Analysis of Large Language Models Reinforcing Dominant Discourses on Gender and Race
Instituto de Estudos da Linguagem (IEL), Universidade Estadual de Campinas (UNICAMP)（语言研究学院（IEL），Campinas州立大学（UNICAMP））；Instituto de Computação (IC), Universidade Estadual de Campinas (UNICAMP)（计算学院（IC），Campinas州立大学（UNICAMP））；Instituto de Informática, Universidade Federal de Goiás (UFG)（信息学院，戈亚斯联邦大学（UFG））
Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMs
MBZUAI（穆罕默德·本·扎耶德人工智能大学）；University of Khartoum（喀土穆大学）；Universität des Saarlandes（萨尔大学）；University of British Columbia（不列颠哥伦比亚大学）；Invertible AI（可逆人工智能公司）；Vector Institute（向量研究所）
NARRA-Gym for Evaluating Interactive Narrative Agents
University of Notre Dame（诺丁汉大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Pennsylvania（宾夕法尼亚大学）；Lehigh University（莱恩大学）；Massachusetts Institute of Technology（麻省理工学院）；Bake AI UC Santa Barbara（加州大学圣芭芭拉分校）；University of Washington（华盛顿大学）
