Complex Reasoning Task
Complex reasoning tasks challenge large language models (LLMs) to perform multi-step inferences and solve problems requiring the integration of diverse knowledge and logical operations. Current research focuses on improving LLMs' reasoning abilities through techniques like chain-of-thought prompting, reinforcement learning with refined credit assignment, and the integration of symbolic reasoning methods with neural networks. These advancements aim to enhance the reliability and generalizability of LLMs for applications ranging from scientific discovery and medical diagnosis to automated problem-solving and decision-making, ultimately contributing to a deeper understanding of artificial intelligence and its potential societal impact.
Papers
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
Fangkai Jiao, Chengwei Qin, Zhengyuan Liu, Nancy F. Chen, Shafiq Joty
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
Alon Jacovi, Yonatan Bitton, Bernd Bohnet, Jonathan Herzig, Or Honovich, Michael Tseng, Michael Collins, Roee Aharoni, Mor Geva
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication
Zhangyue Yin, Qiushi Sun, Cheng Chang, Qipeng Guo, Junqi Dai, Xuanjing Huang, Xipeng Qiu
Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models
Bingshuai Liu, Chenyang Lyu, Zijun Min, Zhanyu Wang, Jinsong Su, Longyue Wang
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Zhuosheng Zhang, Yao Yao, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, Hai Zhao
InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal Large Language Models
Xiaotian Han, Quanzeng You, Yongfei Liu, Wentao Chen, Huangjie Zheng, Khalil Mrini, Xudong Lin, Yiqi Wang, Bohan Zhai, Jianbo Yuan, Heng Wang, Hongxia Yang