Summary of Can Language Models Serve As Text-based World Simulators?, by Ruoyao Wang et al.
Can Language Models Serve as Text-Based World Simulators?
by Ruoyao Wang, Graham Todd, Ziang Xiao, Xingdi Yuan, Marc-Alexandre Côté, Peter Clark, Peter Jansen
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the potential of language models (LLMs) as virtual environment simulators for complex planning and decision-making tasks in text-based games. Current benchmarks are expensive and time-consuming to build by hand, making LLMs a promising alternative. The authors create a new benchmark, ByteSized32-State-Prediction, which contains text game state transitions and accompanying game tasks. They test GPT-4 on this dataset and find that while it performs well, it is still unreliable as a world simulator without further innovations. This work contributes new insights into LLM capabilities and weaknesses, as well as a novel benchmark to track future progress. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at whether language models can help us build virtual worlds for games by predicting how actions change things in the game. Right now, building these kinds of simulations takes a lot of time and money. The researchers want to know if LLMs can do this job instead. They created a new test, called ByteSized32-State-Prediction, which is like a puzzle book for language models. They tried using GPT-4 on this test and found that it’s good at some things but not great at being a world simulator. |
Keywords
» Artificial intelligence » Gpt
