# On Benchmarking Human-Like Intelligence in Machines

arXiv: https://arxiv.org/abs/2502.20502 · HTML: https://mlsanity.com/paper/2502.20502/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 134 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 30 of 53)

- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Fine-Grained Human Feedback Gives Better Rewards for Language Model Training](https://mlsanity.com/paper/2306.01693/) (2023)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [A Survey of Confidence Estimation and Calibration in Large Language Models](https://mlsanity.com/paper/2311.08298/) (2023)
- [Human uncertainty makes classification more robust](https://mlsanity.com/paper/1908.07086/) (2019)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [A Roadmap to Pluralistic Alignment](https://mlsanity.com/paper/2402.05070/) (2024)
- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [Personality Traits in Large Language Models](https://mlsanity.com/paper/2307.00184/) (2023)
- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks](https://mlsanity.com/paper/2305.10160/) (2023)
- [Thinking Fast and Slow in Large Language Models](https://mlsanity.com/paper/2212.05206/) (2022)
- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [Evaluating Human-Language Model Interaction](https://mlsanity.com/paper/2212.09746/) (2022)
- [MMToM-QA: Multimodal Theory of Mind Question Answering](https://mlsanity.com/paper/2401.08743/) (2024)
- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Human alignment of neural network representations](https://mlsanity.com/paper/2211.01201/) (2022)
- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [ToMBench: Benchmarking Theory of Mind in Large Language Models](https://mlsanity.com/paper/2402.15052/) (2024)
- [Evaluating Language Models for Mathematics through Interactions](https://mlsanity.com/paper/2306.01694/) (2023)
- [CogBench: a large language model walks into a psychology lab](https://mlsanity.com/paper/2402.18225/) (2024)
- [In conversation with Artificial Intelligence: aligning language models with human values](https://mlsanity.com/paper/2209.00731/) (2022)
- [Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks](https://mlsanity.com/paper/2205.05718/) (2022)
- [Rehearsal: Simulating Conflict to Teach Conflict Resolution](https://mlsanity.com/paper/2309.12309/) (2023)
- [Auxiliary task demands mask the capabilities of smaller language models](https://mlsanity.com/paper/2404.02418/) (2024)
- [Large Language Models Assume People are More Rational than We Really are](https://mlsanity.com/paper/2406.17055/) (2024)

## Built on by (most cited first, 25 of 25)

- [SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors](https://mlsanity.com/paper/2510.17516/) (2025)
- [BEARCUBS: A benchmark for computer-using web agents](https://mlsanity.com/paper/2503.07919/) (2025)
- [Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective](https://mlsanity.com/paper/2504.10839/) (2025)
- [Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems](https://mlsanity.com/paper/2505.17968/) (2025)
- [Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models](https://mlsanity.com/paper/2507.12547/) (2025)
- [Simulating Society Requires Simulating Thought](https://mlsanity.com/paper/2506.06958/) (2025)
- [From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychology](https://mlsanity.com/paper/2506.16697/) (2025)
- [HumanLLM: Towards Personalized Understanding and Simulation of Human Nature](https://mlsanity.com/paper/2601.15793/) (2026)
- [Using LLMs to Advance the Cognitive Science of Collectives](https://mlsanity.com/paper/2506.00052/) (2025)
- [Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-The-Fly](https://mlsanity.com/paper/2506.16755/) (2025)
- [On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts](https://mlsanity.com/paper/2509.06952/) (2025)
- [A large-scale evaluation of commonsense knowledge in humans and large language models](https://mlsanity.com/paper/2505.10309/) (2025)
- [HugAgent: A Human Simulation Benchmark for Individual-Level Reasoning](https://mlsanity.com/paper/2510.15144/) (2025)
- [HumanStudy-Bench: Towards AI Agent Design for Participant Simulation](https://mlsanity.com/paper/2602.00685/) (2026)
- [V-VAE: A Variational Auto Encoding Framework Towards Fine-Grained Control over Human-Like Chat](https://mlsanity.com/paper/2506.01524/) (2025)
- [NiceWebRL: a Python library for human subject experiments with reinforcement learning environments](https://mlsanity.com/paper/2508.15693/) (2025)
- [Uncovering the Computational Ingredients of Human-Like Representations in LLMs](https://mlsanity.com/paper/2510.01030/) (2025)
- [Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitioners](https://mlsanity.com/paper/2602.11342/) (2026)
- [Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality](https://mlsanity.com/paper/2505.19376/) (2025)
- [What's in the Box? Reasoning about Unseen Objects from Multimodal Cues](https://mlsanity.com/paper/2506.14212/) (2025)
- [Reversing the Lens: Using Explainable AI to Understand Human Expertise](https://mlsanity.com/paper/2510.13814/) (2025)
- [Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings](https://mlsanity.com/paper/2601.11049/) (2026)
- [Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agents](https://mlsanity.com/paper/2602.05597/) (2026)
- [CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition](https://mlsanity.com/paper/2609.21259/) (2026)
- [Multi-System Cognitive Steering for Generative Communication](https://mlsanity.com/paper/2610.19177/) (2026)

## Abstract (arXiv)

Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition. However, we argue that many current evaluation paradigms for AI are insufficient for assessing human-like cognitive capabilities in these models. We identify a set of key shortcomings: a lack of human-validated labels, inadequate representation of human response variability and uncertainty, and reliance on simplified and ecologically invalid tasks. We support our claims by conducting a human evaluation study on nine existing AI benchmarks, suggesting major limitations in task and label designs. To address these limitations, we propose five concrete recommendations for future AI evaluation efforts that will enable more rigorous and meaningful understanding of human-like cognitive capacities in AI models.