A Survey of Reasoning with Foundation Models
- URL: http://arxiv.org/abs/2312.11562v5
- Date: Thu, 25 Jan 2024 11:20:16 GMT
- Title: A Survey of Reasoning with Foundation Models
- Authors: Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu,
Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai
Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Wu Yuan,
Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhang, Pheng Ann Heng,
Jifeng Dai, Ping Luo, Jingdong Wang, Ji-Rong Wen, Xipeng Qiu, Yike Guo, Hui
Xiong, Qun Liu, Zhenguo Li
- Abstract summary: Reasoning plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
We introduce seminal foundation models proposed or adaptable for reasoning.
We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models.
- Score: 235.7288855108172
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Affective Computing Has Changed: The Foundation Model Disruption [47.88090382507161]
We aim to raise awareness of the power of Foundation Models in the field of Affective Computing.
We synthetically generate and analyse multimodal affective data, focusing on vision, linguistics, and speech (acoustics)
We discuss some fundamental problems, such as ethical issues and regulatory aspects, related to the use of Foundation Models in this research area.
arXiv Detail & Related papers (2024-09-13T15:20:18Z) - Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models [79.04590934264235]
Vision-and-Language Navigation (VLN) has gained increasing attention over recent years.
Foundation models have shaped the challenges and proposed methods for VLN research.
arXiv Detail & Related papers (2024-07-09T16:53:36Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - The Essential Role of Causality in Foundation World Models for Embodied AI [102.75402420915965]
Embodied AI agents will require the ability to perform new tasks in many different real-world environments.
Current foundation models fail to accurately model physical interactions and are therefore insufficient for Embodied AI.
The study of causality lends itself to the construction of veridical world models.
arXiv Detail & Related papers (2024-02-06T17:15:33Z) - Training and Serving System of Foundation Models: A Comprehensive Survey [32.0115390377174]
This paper extensively explores the methods employed in training and serving foundation models from various perspectives.
It provides a detailed categorization of these state-of-the-art methods, including finer aspects such as network, computing, and storage.
arXiv Detail & Related papers (2024-01-05T05:27:15Z) - Intrinsic Motivation in Model-based Reinforcement Learning: A Brief
Review [77.34726150561087]
This review considers the existing methods for determining intrinsic motivation based on the world model obtained by the agent.
The proposed unified framework describes the architecture of agents using a world model and intrinsic motivation to improve learning.
arXiv Detail & Related papers (2023-01-24T15:13:02Z) - Towards Reasoning in Large Language Models: A Survey [11.35055307348939]
It is not yet clear to what extent large language models (LLMs) are capable of reasoning.
This paper provides a comprehensive overview of the current state of knowledge on reasoning in LLMs.
arXiv Detail & Related papers (2022-12-20T16:29:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.