Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·21h ago
AI Research
arXiv CS.AI·Jun 29·5 min read
From Tokens to States: LLMs as a Special Case of World Models and the Continuous Path Beyond
TL;DR
Large Language Models (LLMs) have limitations in understanding complex states of the world, which hinders their effectiveness. This research presents a framework that positions LLMs as a specific type of world model, enabling a continuous transition to more advanced models.
✦ Why It Matters
Engineers can leverage this framework to improve LLMs for more complex real-world applications.
Key Takeaways
- Consider LLMs as a subset of broader world models.
- Investigate multi-token prediction to improve model capabilities.
- Address data challenges transitioning to action-labeled environments.
- Evaluate if transformers can adapt to continuous-state predictions.
How It Works
LLMs operate by predicting the next token in a sequence, while world models encompass a wider range of actions and states. By moving along a spectrum from token prediction to more complex predictions, researchers can develop models that better simulate reality.