5 papers
Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks
Kentaro Ozeki, Risako Ando, Takanobu Morishita +3
Large reasoning models (LRMs) that leverage test-time computation achieve strong performance on mathematical and scientific benchmarks and are increasingly deployed in real-world a…
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…
Normative Reasoning in Large Language Models: A Comparative Benchmark from Logical and Modal Perspectives
Kentaro Ozeki, Risako Ando, Takanobu Morishita +3
Normative reasoning is a type of reasoning that involves normative or deontic modality, such as obligation and permission. While large language models (LLMs) have demonstrated rema…
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…