# Deterministic or probabilistic? The psychology of LLMs as random number generators

arXiv: https://arxiv.org/abs/2502.19965 · HTML: https://mlsanity.com/paper/2502.19965/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [LLaMA Beyond English: An Empirical Study on Language Capability Transfer](https://mlsanity.com/paper/2401.01055/) (2024)
- [Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages](https://mlsanity.com/paper/2404.11553/) (2024)
- [Benchmarking Linguistic Diversity of Large Language Models](https://mlsanity.com/paper/2412.10271/) (2024)
- [MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs](https://mlsanity.com/paper/2406.07243/) (2024)
- [An In-depth Look at Gemini's Language Abilities](https://mlsanity.com/paper/2312.11444/) (2023)
- [Heads, Tails, and AI Fails: LLMs, Randomness, and Human Judgments](https://mlsanity.com/paper/2406.00092/) (2024)
- [What an Elegant Bridge: Multilingual LLMs are Biased Similarly in Different Languages](https://mlsanity.com/paper/2407.09704/) (2024)

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

- [One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions](https://mlsanity.com/paper/2607.10252/) (2026)
- [FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences](https://mlsanity.com/paper/2606.03330/) (2026)
- [Evaluating book summaries from internal knowledge in Large Language Models: a cross-model and semantic consistency approach](https://mlsanity.com/paper/2503.21613/) (2025)
- [Stochastic Streets: A Walk Through Random LLM Address Generation in four European Cities](https://mlsanity.com/paper/2509.12914/) (2025)
- [The Randomness Floor: Measuring Intrinsic Non-Randomness in Language Model Token Distributions](https://mlsanity.com/paper/2604.22771/) (2026)
- [UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs](https://mlsanity.com/paper/2606.06622/) (2026)
- [Mitigating Scoring Bias in LLM-as-a-Judge via Random Number Generation](https://mlsanity.com/paper/2608.05726/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have transformed text generation through inherently probabilistic context-aware mechanisms, mimicking human natural language. In this paper, we systematically investigate the performance of various LLMs when generating random numbers, considering diverse configurations such as different model architectures, numerical ranges, temperature, and prompt languages. Our results reveal that, despite their stochastic transformers-based architecture, these models often exhibit deterministic responses when prompted for random numerical outputs. In particular, we find significant differences when changing the model, as well as the prompt language, attributing this phenomenon to biases deeply embedded within the training data. Models such as DeepSeek-R1 can shed some light on the internal reasoning process of LLMs, despite arriving to similar results. These biases induce predictable patterns that undermine genuine randomness, as LLMs are nothing but reproducing our own human cognitive biases.