# A Comparison of Large Language Model and Human Performance on Random Number Generation Tasks

arXiv: https://arxiv.org/abs/2408.09656 · HTML: https://mlsanity.com/paper/2408.09656/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023)
- [Large language models predict human sensory judgments across six modalities](https://mlsanity.com/paper/2302.01308/) (2023)

## Built on by (most cited first, 7 of 7)

- [The Illusion of Stochasticity in LLMs](https://mlsanity.com/paper/2604.06543/) (2026)
- [Playing games with Large language models: Randomness and strategy](https://mlsanity.com/paper/2503.02582/) (2025)
- [One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions](https://mlsanity.com/paper/2607.10252/) (2026)
- [LLMs Prompted for Graphs: Hallucinations and Generative Capabilities](https://mlsanity.com/paper/2409.00159/) (2024)
- [FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences](https://mlsanity.com/paper/2606.03330/) (2026)
- [Large Language Models Can Take False First Steps at Inference-time Planning](https://mlsanity.com/paper/2602.02991/) (2026)
- [In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models](https://mlsanity.com/paper/2605.23908/) (2026)

## Abstract (arXiv)

Random Number Generation Tasks (RNGTs) are used in psychology for examining how humans generate sequences devoid of predictable patterns. By adapting an existing human RNGT for an LLM-compatible environment, this preliminary study tests whether ChatGPT-3.5, a large language model (LLM) trained on human-generated text, exhibits human-like cognitive biases when generating random number sequences. Initial findings indicate that ChatGPT-3.5 more effectively avoids repetitive and sequential patterns compared to humans, with notably lower repeat frequencies and adjacent number frequencies. Continued research into different models, parameters, and prompting methodologies will deepen our understanding of how LLMs can more closely mimic human random generation behaviors, while also broadening their applications in cognitive and behavioral science research.