Rationality Check! Benchmarking the Rationality of Large Language Models

• URL: http://arxiv.org/abs/2509.14546v1
• Date: Thu, 18 Sep 2025 02:23:56 GMT
• Title: Rationality Check! Benchmarking the Rationality of Large Language Models
• Authors: Zhilun Zhou, Jing Yi Wang, Nicholas Sukiennik, Chen Gao, Fengli Xu, Yong Li, James Evans,
• Abstract summary: We propose the first benchmark for evaluating the omnibus rationality of large language models (LLMs)<n>The benchmark includes an easy-to-use toolkit, extensive experimental results, and analysis that illuminates where LLMs converge and diverge from idealized human rationality.
• Score: 15.612266760229945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs), a recent advance in deep learning and machine intelligence, have manifested astonishing capacities, now considered among the most promising for artificial general intelligence. With human-like capabilities, LLMs have been used to simulate humans and serve as AI assistants across many applications. As a result, great concern has arisen about whether and under what circumstances LLMs think and behave like real human agents. Rationality is among the most important concepts in assessing human behavior, both in thinking (i.e., theoretical rationality) and in taking action (i.e., practical rationality). In this work, we propose the first benchmark for evaluating the omnibus rationality of LLMs, covering a wide range of domains and LLMs. The benchmark includes an easy-to-use toolkit, extensive experimental results, and analysis that illuminates where LLMs converge and diverge from idealized human rationality. We believe the benchmark can serve as a foundational tool for both developers and users of LLMs.

Related papers

• Mixing Expert Knowledge: Bring Human Thoughts Back To the Game of Go [74.28228642327726]
  Large language models (LLMs) have demonstrated exceptional performance in reasoning tasks such as mathematics and coding.<n>LoGos is a powerful LLM that not only maintains outstanding general reasoning abilities, but also conducts Go gameplay in natural language.<n>LoGos achieves performance comparable to human professional players, substantially surpassing all existing LLMs.
  arXiv  Detail & Related papers  (2026-01-23T05:00:49Z)
• Multi-Agent Evolve: LLM Self-Improve through Co-evolution [53.00458074754831]
  Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs)<n>Recent Self-Play RL methods, inspired by the success of the paradigm in games and Go, aim to enhance LLM reasoning capabilities without human-annotated data.<n>We propose Multi-Agent Evolve (MAE), a framework that enables LLMs to self-evolve in solving diverse tasks, including mathematics, reasoning, and general knowledge Q&A.
  arXiv  Detail & Related papers  (2025-10-27T17:58:02Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence.<n>WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions [6.201550639431176]
  This paper focuses on the burgeoning prominence of large language models (LLMs) We underscore the pivotal role of Reinforcement Learning from Human Feedback (RLHF) in augmenting LLMs' rationality and decision-making prowess.
  arXiv  Detail & Related papers  (2024-03-14T18:36:04Z)
• Should We Fear Large Language Models? A Structural Analysis of the Human Reasoning System for Elucidating LLM Capabilities and Risks Through the Lens of Heidegger's Philosophy [0.0]
  This study investigates the capabilities and risks of Large Language Models (LLMs) It uses the innovative parallels between the statistical patterns of word relationships within LLMs and Martin Heidegger's concepts of "ready-to-hand" and "present-at-hand" Our findings reveal that while LLMs possess the capability for Direct Explicative Reasoning and Pseudo Rational Reasoning, they fall short in authentic rational reasoning and have no creative reasoning capabilities.
  arXiv  Detail & Related papers  (2024-03-05T19:40:53Z)
• LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
  Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication. We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies. We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
  arXiv  Detail & Related papers  (2024-02-13T19:46:39Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales [62.02328001381361]
  We show that human utility of existing rationales is far from satisfactory, and expensive to estimate with human studies. We translate this finding into an automated score, GEN-U, that can help improve LMs' ability to generate rationales with better human utility.
  arXiv  Detail & Related papers  (2023-05-11T19:01:13Z)
• Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs [0.0]
  This study aims to investigate the performance of large language models (LLMs) on different reasoning tasks. My findings indicate that LLMs excel at analogical and moral reasoning, yet struggle to perform as proficiently on spatial reasoning tasks.
  arXiv  Detail & Related papers  (2023-03-22T22:53:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.