Reframing a negative into a positive thought is at the crux of several cognitive approaches to mental health and psychotherapy that could be made more accessible by large language model-based solutions. Such reframing is typically non-trivial and requires multiple rationalization steps to uncover the underlying issue of a negative thought and transform it to be more positive. However, this rationalization process is currently neglected by both datasets and models which reframe thoughts in one step. In this work, we address this gap by augmenting open-source datasets for positive text rewriting with synthetically-generated Socratic rationales using a novel framework called \textsc{SocraticReframe}. SocraticReframe uses a sequence of question-answer pairs to rationalize the thought rewriting process. We show that such Socratic rationales significantly improve positive text rewriting for different open-source LLMs according to both automatic and human evaluations guided by criteria from psychotherapy research. We validate our framework and the synthetic rationalizations with expert judgements from domain experts and psychology students in an IRB-approved annotation study. Our findings highlight the potential of utilizing the synergy between LLM reasoning and established psychotherapy techniques to build assistive solutions for reframing negative thoughts.
Socratic-PRMBench: Benchmarking Process Reward Models with Systematic Reasoning Patterns
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tongyi Lab, Alibaba Group（阿里云实验室）
Socratic-Zero : Bootstrapping Reasoning via Data-Free Agent Co-evolution
Alibaba Group Holding Limited（阿里巴巴集团控股有限公司）；EPIC Lab, Shanghai Jiao Tong University（上海交通大学EPIC实验室）；Shanghai University of Finance and Economics（上海财经大学）；Wuhan University（武汉大学）；Zhejiang University（浙江大学）
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Meituan（美团）；Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究所）；Key Laboratory of Intelligent Information Processing, Fudan University（复旦大学智能信息处理重点实验室）；Macau University of Science and Technology（澳门科技大学）
Southeast University（东南大学）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education（新一代人工智能技术及其交叉应用国家重点实验室）；China Mobile Research Institute（中国移动研究院）；Monash University（墨尔本大学）；University of Manchester（曼彻斯特大学）；University of Edinburgh（爱丁堡大学）
When Compression Helps and When It Hurts: Condition-Aware Analysis of Chain-of-Thought Distillation
Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo（宁波数字孪生研究院，东方理工高等研究院，宁波）；Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院）；The Hong Kong Polytechnic University（香港理工大学）；LMU Munich（慕尼黑大学）；Saarland University（萨尔大学）
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Key Laboratory of Machine Perceptron (MOE), Peking University（北京大学机器感知教育部重点实验室）
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；National University of Singapore（新加坡国立大学）；University of Electronic Science and Technology of China（电子科技大学）；Beijing University of Posts and Telecommunieations（北京邮电大学）；Singapore Management University（新加坡管理大学）
