2 papers
cs.CL2024
Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus
Seungpil Lee, Woochang Sim, Donghyeon Shin +6
The existing methods for evaluating the inference abilities of Large Language Models (LLMs) have been predominantly results-centric, making it challenging to assess the inference p…
cs.AI2024
ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning
Hosung Lee, Sejin Kim, Seungpil Lee +4
This paper introduces ARCLE, an environment designed to facilitate reinforcement learning research on the Abstraction and Reasoning Corpus (ARC). Addressing this inductive reasonin…