Summary of Latent State Estimation Helps Ui Agents to Reason, by William E Bishop et al.
Latent State Estimation Helps UI Agents to Reason
by William E Bishop, Alice Li, Christopher Rawles, Oriana Riva
First submitted to arxiv on: 17 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 A recent study investigates whether Large Language Models (LLMs) can build estimates of latent state in real-world environments, where the response to their actions is non-deterministic and observed through noise. The authors find that prompting LLMs in a zero-shot manner enables them to form point estimates of latent state in a textual space. They then demonstrate that LLMs used in this way can accurately infer various aspects of latent state, such as performed versus commanded actions and task progression, with an accuracy of over 76%. The authors also show that LLM-powered agents that explicitly estimate and reason about latent state are able to successfully complete up to 1.6x more tasks than those that do not. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs can learn from the world around them! Imagine a computer program that helps control devices, like your phone or TV. This program needs to understand what’s happening in the world, even if things don’t always go exactly as planned. Researchers wanted to see if big language models (LLMs) could figure out what’s going on when things get messy. They found that by asking LLMs the right questions, they can make good guesses about what’s really happening. This is important because it means computers might be able to do more tasks on their own, without needing humans to tell them exactly what to do. |
Keywords
» Artificial intelligence » Prompting » Zero shot
