Effective communication often relies on aligning a message with an audience's narrative and worldview. Narrative shift involves transforming text to reflect a different narrative framework while preserving its original core message--a task we demonstrate is significantly challenging for current Large Language Models (LLMs). To address this, we propose a neurosymbolic approach grounded in social science theory and abductive reasoning. Our method automatically extracts rules to abduce the specific story elements needed to guide an LLM through a consistent and targeted narrative transformation. Across multiple LLMs, abduction-guided transformed stories shifted the narrative while maintaining the fidelity with the original story. For example, with GPT-4o we outperform the zero-shot LLM baseline by 55.88% for collectivistic to individualistic narrative shift while maintaining superior semantic similarity with the original stories (40.4% improvement in KL divergence). For individualistic to collectivistic transformation, we achieve comparable improvements. We show similar performance across both directions for Llama-4, and Grok-4 and competitive performance for Deepseek-R1.
Allen Institute for AI（艾伦人工智能研究院）；Facebook AI（脸书人工智能研究院）；Paul G. Allen School of Computer Science & Engineering（保罗·G·艾伦计算机科学与工程学院）
溯因推理是对最合理解释的推理。例如，如果珍妮下班回家后发现家里一片狼借，她记得自己开着一扇窗户，她可以假设一个小偷闯入了她的房子，并造成了混乱，这是最合理的解释。虽然溯因一直被认为是人们如何解释和阅读自然语言字里行间的核心（Hobbs et al.，1988年），在支持溯因自然语言推理和生成方面的研究相对较少。我们提出的第一项研究，调查基于语言的溯因推理的可行性。我们引入了一个挑战数据集ART，它由超过20 k个常识性的叙事上下文和200 k个解释组成。基于这个数据集，我们概念化两个新的任务-（i）外展NLI：一个多项选择问答任务，选择更可能的解释，和（ii）外展NLG：一个条件生成任务，解释给定的观察自然语言。在外展NLI上，最佳模型的准确率为68.9%，远低于人类的91.4%。在外展NLG上，目前最好的语言生成器甚至更加挣扎，因为它们缺乏对人类来说微不足道的推理能力。我们的分析导致了对深度预训练的语言模型未能执行的推理类型的新见解-尽管它们在相关但定义更窄的NLI任务上表现出色-为未来的研究指明了有趣的途径。
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；College of Software, Jilin University（吉林大学软件学院）；Xingchen AGI Lab, China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd.（中国电信人工智能技术（北京）有限公司星辰AGI实验室）；Bilibili Inc.（哔哩哔哩公司）；University of Ottawa（渥太华大学）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）
Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment
Institute of Data Science, National University of Singapore, Singapore（新加坡国立大学数据科学研究所）；National University Hospital, Singapore（新加坡国立大学医院）；Saw Swee Hock School of Public Health, National University of Singapore, Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）
Retrieval-Augmented Framework for LLM-Based Clinical Decision Support
Dept. of Computer Science, The University of Texas at El Paso, USA（计算机科学系，德克萨斯大学埃尔帕索分校）；Dept. of Emergency Medicine, University of Maryland School of Medicine, USA（急诊医学系，马里兰大学医学院）；Dept. of Public Health Sciences, The University of Texas at El Paso, USA（公共卫生科学系，德克萨斯大学埃尔帕索分校）
CML-Bench: A Framework for Evaluating and Enhancing LLM-Powered Movie Scripts Generation
The Hong Kong University of Science and Technology（香港科学与技术大学）；Lehigh University（莱斯大学）；Fudan University（复旦大学）；Emory University（埃默里大学）；University of Central Florida（中央佛罗里达大学）
Coherence in the brain unfolds across separable temporal regimes
Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland（大学精神病学与心理学系，苏黎世大学，苏黎世，瑞士）；Department of Translation and Language Sciences, University Pompeu Fabra, Barcelona, Spain（翻译与语言科学系，庞培法拉大学，巴塞罗那，西班牙）；Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland（神经放射学系，临床神经科学中心，苏黎世大学医院，苏黎世，瑞士）；Department of Psychology, University of Oslo, Oslo, Norway（心理学系，奥斯陆大学，奥斯陆，挪威）；Cognitive and Affective Neuroscience Unit, University of Zurich, Zurich, Switzerland（认知与情感神经科学单元，苏黎世大学，苏黎世，瑞士）；Center for Clinical Neuroscience and Cognition, University of Groningen, Groningen, Netherlands（临床神经科学与认知中心，格罗宁根大学，格罗宁根，荷兰）；Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland（苏黎世神经科学中心，苏黎世大学和ETH苏黎世分校，苏黎世，瑞士）
