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A Survey of Reasoning with Foundation Models

Authors :
Sun, Jiankai
Zheng, Chuanyang
Xie, Enze
Liu, Zhengying
Chu, Ruihang
Qiu, Jianing
Xu, Jiaqi
Ding, Mingyu
Li, Hongyang
Geng, Mengzhe
Wu, Yue
Wang, Wenhai
Chen, Junsong
Yin, Zhangyue
Ren, Xiaozhe
Fu, Jie
He, Junxian
Yuan, Wu
Liu, Qi
Liu, Xihui
Li, Yu
Dong, Hao
Cheng, Yu
Zhang, Ming
Heng, Pheng Ann
Dai, Jifeng
Luo, Ping
Wang, Jingdong
Wen, Ji-Rong
Qiu, Xipeng
Guo, Yike
Xiong, Hui
Liu, Qun
Li, Zhenguo
Publication Year :
2023

Abstract

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, e.g., Large Language Models (LLMs), there is a growing interest in exploring their abilities in reasoning tasks. In this paper, we introduce seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models. We also discuss the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning. By discussing these future research directions, we hope to inspire researchers in their exploration of this field, stimulate further advancements in reasoning with foundation models, and contribute to the development of AGI.<br />Comment: 20 Figures, 160 Pages, 750+ References, Project Page https://github.com/reasoning-survey/Awesome-Reasoning-Foundation-Models

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2312.11562
Document Type :
Working Paper