# Bayes in the age of intelligent machines

arXiv: https://arxiv.org/abs/2311.10206 · HTML: https://mlsanity.com/paper/2311.10206/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Recasting Gradient-Based Meta-Learning as Hierarchical Bayes](https://mlsanity.com/paper/1801.08930/) (2018)
- [Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve](https://mlsanity.com/paper/2309.13638/) (2023)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression](https://mlsanity.com/paper/2306.15063/) (2023)
- [Modeling rapid language learning by distilling Bayesian priors into artificial neural networks](https://mlsanity.com/paper/2305.14701/) (2023)
- [Meta-trained agents implement Bayes-optimal agents](https://mlsanity.com/paper/2010.11223/) (2020)
- [One Model for the Learning of Language](https://mlsanity.com/paper/1711.06301/) (2017)
- [Gaussian Process Probes (GPP) for Uncertainty-Aware Probing](https://mlsanity.com/paper/2305.18213/) (2023)
- [Gaussian Process Surrogate Models for Neural Networks](https://mlsanity.com/paper/2208.06028/) (2022)

## Built on by (most cited first, 22 of 22)

- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent](https://mlsanity.com/paper/2412.18174/) (2024)
- [Incoherent Probability Judgments in Large Language Models](https://mlsanity.com/paper/2401.16646/) (2024)
- [Bias Amplification in Language Model Evolution: An Iterated Learning Perspective](https://mlsanity.com/paper/2404.04286/) (2024)
- [Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems](https://mlsanity.com/paper/2505.17968/) (2025)
- [Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice](https://mlsanity.com/paper/2405.19313/) (2024)
- [Shaping Shared Languages: Human and Large Language Models' Inductive Biases in Emergent Communication](https://mlsanity.com/paper/2503.04395/) (2025)
- [Levels of Analysis for Large Language Models](https://mlsanity.com/paper/2503.13401/) (2025)
- [Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions](https://mlsanity.com/paper/2505.11614/) (2025)
- [On Inductive Biases That Enable Generalization of Diffusion Transformers](https://mlsanity.com/paper/2410.21273/) (2024)
- [Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks](https://mlsanity.com/paper/2402.01821/) (2024)
- [Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models](https://mlsanity.com/paper/2504.12585/) (2025)
- [Towards Understanding the Benefits of Neural Network Parameterizations in Geophysical Inversions: A Study With Neural Fields](https://mlsanity.com/paper/2503.17503/) (2025)
- [On Language Models' Sensitivity to Suspicious Coincidences](https://mlsanity.com/paper/2504.09387/) (2025)
- [Prompting as Scientific Inquiry](https://mlsanity.com/paper/2507.00163/) (2025)
- [Modelling the Morphology of Verbal Paradigms: A Case Study in the Tokenization of Turkish and Hebrew](https://mlsanity.com/paper/2602.05648/) (2026)
- [Datasets for Verb Alternations across Languages: BLM Templates and Data Augmentation Strategies](https://mlsanity.com/paper/2603.15295/) (2026)
- [Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures](https://mlsanity.com/paper/2405.20230/) (2024)
- [Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings](https://mlsanity.com/paper/2411.18645/) (2024)
- [Comparing Natural and Synthetic Structured Data: A Study of the Passive Verb Alternation in French and Italian](https://mlsanity.com/paper/2603.25227/) (2026)
- [Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allow](https://mlsanity.com/paper/2610.00679/) (2026)

## Abstract (arXiv)

The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case, and that in fact these systems offer new opportunities for Bayesian modeling. Specifically, we argue that Bayesian models of cognition and artificial neural networks lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, where a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.