2 citations · 2 across the 4 of their papers we have counts for
4 papers
From Noise to Diversity: Random Embedding Injection in LLM Reasoning
Heejun Kim, Seungpil Lee, Jewon Yeom +5
Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned content or from the act of inje…
Can Large Language Models Develop Gambling Addiction?
Seungpil Lee, Donghyeon Shin, Yunjeong Lee +1
This study identifies the specific conditions under which large language models exhibit human-like gambling addiction patterns, providing critical insights into their decision-maki…
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…
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…