LLMs Learn Hidden Markov Models In-Context
A new study from arXiv (2506.07298v3) demonstrates that pre-trained large language models (LLMs) can learn Hidden Markov Models (HMMs) through in-context learning (ICL), achieving near-optimal predictive accuracy on synthetic data. The research reveals novel scaling trends tied to HMM properties and offers theoretical conjectures. On real-world animal decision-making tasks, ICL matches expert-designed models. This is the first evidence that ICL can learn probabilistic graphical models.
Key facts
- arXiv paper 2506.07298v3
- LLMs model HMMs via in-context learning
- Predictive accuracy approaches theoretical optimum on synthetic HMMs
- Novel scaling trends influenced by HMM properties
- Theoretical conjectures for empirical observations
- Practical guidelines for scientists using ICL as diagnostic tool
- Competitive performance on real-world animal decision-making tasks
- First demonstration that ICL can learn probabilistic graphical models
Entities
Institutions
- arXiv