Summary of Stateact: State Tracking and Reasoning For Acting and Planning with Large Language Models, by Nikolai Rozanov and Marek Rei
StateAct: State Tracking and Reasoning for Acting and Planning with Large Language Models
by Nikolai Rozanov, Marek Rei
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 Large language models (LLMs) have made significant progress in solving “real” tasks, such as interacting with online tools and robotics. However, they still struggle with long-range reasoning tasks. To address this issue, we propose a simple method based on few-shot in-context learning, which enhances chain-of-thought with state-tracking for planning and acting with LLMs. Our approach achieves the new state-of-the-art on Alfworld for in-context learning methods (+14% over the previous best) and performs on par with methods that use additional training data and tools like code-execution. We also demonstrate improved performance on longer horizon problems and reduced number of steps required to solve tasks across various LLMs, including API-based and open-source ones. Our method works by leveraging a simple few-shot in-context learning approach based on chain-of-thought with state-tracking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using large language models (LLMs) to solve real-world problems like interacting with online tools or controlling robots. But these models struggle when it comes to thinking ahead and making long-term decisions. To fix this, we developed a simple method that lets LLMs learn from just a few examples and then use that knowledge to make better plans and take action. Our approach outperforms previous methods on a challenging task called Alfworld and is as good as using extra data or special tools. We also show that our method can solve harder problems and find the solution faster than before. This works across different types of LLMs, from those designed for specific tasks to open-source models. |
Keywords
» Artificial intelligence » Few shot » Tracking
