Summary of Language-guided World Models: a Model-based Approach to Ai Control, by Alex Zhang et al.
Language-Guided World Models: A Model-Based Approach to AI Control
by Alex Zhang, Khanh Nguyen, Jens Tuyls, Albert Lin, Karthik Narasimhan
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Language-Guided World Models (LWMs), probabilistic models that can simulate environments by reading texts. Agents equipped with these models enable humans to control them in multiple tasks via natural verbal communication. To develop robust LWMs, the authors design a challenging world modeling benchmark based on the game MESSENGER and evaluate various Transformer-based models. The results show that while the state-of-the-art Transformer model improves simulation quality over a no-text baseline, it lacks generalizability. By fusing the Transformer with EMMA attention mechanism, the authors devise a more robust model that substantially outperforms the Transformer and approaches performance of an oracle semantic parsing and grounding capability. This breakthrough has implications for improving AI safety and transparency by enabling agents to present plans to humans before execution and revise them based on language feedback. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating special models that can understand and follow text instructions. Imagine having a robot or computer program that you can control just by talking to it! The researchers designed a test to see how well these models work, using a game called MESSENGER as an example. They found that the best model they tried was still not very good at understanding complex language instructions. To fix this problem, they combined two different approaches and got much better results. This is important because it could help make artificial intelligence (AI) more safe and transparent by allowing humans to give instructions and adjust them as needed. |
Keywords
* Artificial intelligence * Attention * Grounding * Semantic parsing * Transformer
