# Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefs

arXiv: https://arxiv.org/abs/2504.13644 · HTML: https://mlsanity.com/paper/2504.13644/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"](https://mlsanity.com/paper/2309.12288/) (2023)
- [ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs](https://mlsanity.com/paper/2410.12405/) (2024)
- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling](https://mlsanity.com/paper/2311.08718/) (2023)
- [Evaluating Superhuman Models with Consistency Checks](https://mlsanity.com/paper/2306.09983/) (2023)

## Built on by (most cited first, 9 of 9)

- [When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs](https://mlsanity.com/paper/2602.06286/) (2026)
- [Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLMs](https://mlsanity.com/paper/2509.10739/) (2025)
- [Beyond Calibration: Do a Typed-Decision Model's Probabilities Obey the Probability Axioms?](https://mlsanity.com/paper/2609.33209/) (2026)
- [The Effect of Belief Boxes and Open-mindedness on Persuasion](https://mlsanity.com/paper/2512.06573/) (2025)
- [Neurosymbolic Learning for Inference-Time Argumentation](https://mlsanity.com/paper/2605.20098/) (2026)
- [Can LLMs Take Retrieved Information with a Grain of Salt?](https://mlsanity.com/paper/2605.06919/) (2026)
- [Superficial Beliefs in LLM Decision-Making](https://mlsanity.com/paper/2606.11016/) (2026)
- [OptimismBench: Forecasting Bias and the Alignment Effect in Language Model Judgment](https://mlsanity.com/paper/2607.26981/) (2026)
- [Training Language Models To Be Coherent Decision-Makers](https://mlsanity.com/paper/2610.09164/) (2026)

## Abstract (arXiv)

Advances in the general capabilities of large language models (LLMs) have led to their use for information retrieval, and as components in automated decision systems. A faithful representation of probabilistic reasoning in these models may be essential to ensure trustworthy, explainable and effective performance in these tasks. Despite previous work suggesting that LLMs can perform complex reasoning and well-calibrated uncertainty quantification, we find that current versions of this class of model lack the ability to provide rational and coherent representations of probabilistic beliefs. To demonstrate this, we introduce a novel dataset of claims with indeterminate truth values and apply a number of well-established techniques for uncertainty quantification to measure the ability of LLM's to adhere to fundamental properties of probabilistic reasoning.