Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a mental model of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our system, CURIOUS, achieves a new state-of-the-art on three different defeasible reasoning datasets. This result is significant as it illustrates that performance can be improved by guiding a system to "think about" a question and explicitly model the scenario, rather than answering reflexively. Code, data, and pre-trained models are located at https://github.com/madaan/thinkaboutit.
Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses. Existing MADP models usually imitate action combinations from the labeled multi-action dialog samples. Due to data limitations, they generalize poorly toward unseen dialog flows. While interactive learning and reinforcement learning algorithms can be applied to incorporate external data sources of real users and user simulators, they take significant manual effort to build and suffer from instab…
Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost
NLP（自然语言处理）；CT Lab, Department of Computer and Information Science, University of Macau（计算机与信息科学系，澳门大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（教育部新一代智能搜索与推荐工程研究中心）；Tencent Jarvis Lab（腾讯杰维斯实验室）；Medical Artificial Intelligence Lab, Westlake University（西湖大学医学人工智能实验室）
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do
The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统认知与决策智能实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Think Consistently, Reason Efficiently: Energy-Based Calibration for Implicit Chain-of-Thought
University of Oxford（牛津大学）；Tsinghua University（清华大学）；Tencent（腾讯）；Southern University of Science and Technology（南方科技大学）；National University of Singapore（新加坡国立大学）
PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
Center for Computational Science and Engineering（计算科学与工程中心）；Schmarzman College of Computing（施马茨曼计算学院）；Laboratory for Atomistic and Molecular Mechanics (LAMM)（原子与分子力学实验室）；Massachusetts Institute of Technology（麻省理工学院）
