April 2, 2024, 7:52 p.m. | Man Luo, Shrinidhi Kumbhar, Ming shen, Mihir Parmar, Neeraj Varshney, Pratyay Banerjee, Somak Aditya, Chitta Baral

cs.CL updates on arXiv.org arxiv.org

arXiv:2310.00836v3 Announce Type: replace
Abstract: Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and Reasoning (KR) systems that did not scale and required non-trivial manual effort. Recently, the emergence of large language models (LLMs) has demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. Consequently, there's a growing interest in using LLMs for logical reasoning via natural language. This work strives to …

abstract artificial artificial intelligence arxiv benchmark capabilities challenge cs.ai cs.cl domain emergence humans intelligence knowledge language language models reasoning representation researchers scale survey systems type

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Lead Data Scientist, Commercial Analytics

@ Checkout.com | London, United Kingdom

Data Engineer I

@ Love's Travel Stops | Oklahoma City, OK, US, 73120