2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Abductive Reasoning with Syllogistic Forms in Large Language Models
Hirohiko Abe, Risako Ando, Takanobu Morishita Kentaro Ozeki +2
Research in AI using Large-Language Models (LLMs) is rapidly evolving, and the comparison of their performance with human reasoning has become a key concern. Prior studies have ind…
Evaluation of Deontic Conditional Reasoning in Large Language Models: The Case of Wason's Selection Task
Hirohiko Abe, Kentaro Ozeki, Risako Ando +3
As large language models (LLMs) advance in linguistic competence, their reasoning abilities are gaining increasing attention. In humans, reasoning often performs well in domain spe…
Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Dataset
Kentaro Ozeki, Risako Ando, Takanobu Morishita +3
This paper explores the question of how accurately current large language models can perform logical reasoning in natural language, with an emphasis on whether these models exhibit…
Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases
Risako Ando, Takanobu Morishita, Hirohiko Abe +2
This paper investigates whether current large language models exhibit biases in logical reasoning, similar to humans. Specifically, we focus on syllogistic reasoning, a well-studie…