The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from another one. However, in most NLP applications only single source sentences instead of sentence pairs are available. Hence, we propose a new task that measures how well a model can generate an entailed sentence from a source sentence. We take entailment-pairs of the Stanford Natural Language Inference corpus and train an LSTM with attention. On a manually annotated test set we found that 82% of generated sentences are correct, an improvement of 10.3% over an LSTM baseline. A qualitative analysis shows that this model is not only capable of shortening input sentences, but also inferring new statements via paraphrasing and phrase entailment. We then apply this model recursively to input-output pairs, thereby generating natural language inference chains that can be used to automatically construct an entailment graph from source sentences. Finally, by swapping source and target sentences we can also train a model that given an input sentence invents additional information to generate a new sentence.
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
Center for Advanced Intelligence Project, RIKEN（理化学研究所先进智能项目中心）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs
Ubiquitous Knowledge Processing Lab (UKP Lab), TU Darmstadt and ATHENE National Research Center for Applied Cybersecurity, Germany（图宾根大学知识处理实验室（UKP Lab）及德国应用网络安全国家研究中心ATHENE）；Dept. of ECE & Ingenuity Labs Research Institute, Queen’s University, Canada（皇后大学电子工程系及创新实验室研究机构）；University of Bath, UK（巴斯大学）
Generating Storytelling Images with Rich Chains-of-Reasoning
X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, China（X-LANCE实验室，计算机科学学院，上海交通大学，中国）；Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室，中国）；East China University of Technology, China（东华大学，中国）；University of Texas at Arlington, USA（德克萨斯大学阿灵顿分校，美国）
Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study
School of Informatics, Xiamen University, China（厦门大学信息学院）；Baidu Inc., Beijing, China（百度公司）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
