Summary of Llms in the Imaginarium: Tool Learning Through Simulated Trial and Error, by Boshi Wang et al.
LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
by Boshi Wang, Hao Fang, Jason Eisner, Benjamin Van Durme, Yu Su
First submitted to arxiv on: 7 Mar 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 The proposed biologically inspired method for tool-augmented large language models (LLMs) addresses the critical aspect of accuracy in using tools for which they have been trained. Existing LLMs, including GPT-4 and fine-tuned open-source models, only achieve a correctness rate of 30% to 60%, far from reliable use. The method, simulated trial and error (STE), orchestrates three key mechanisms: trial and error, imagination, and memory. STE leverages an LLM’s imagination to simulate plausible scenarios for using a tool, followed by interaction with the tool to learn from execution feedback. Both short-term and long-term memory are employed to improve exploration depth and breadth. The approach is evaluated on ToolBench, demonstrating a significant boost in tool learning accuracy under both in-context learning and fine-tuning settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools that can help humans learn and understand the world better. But for them to do this effectively, they need to be able to use other tools properly. Unfortunately, existing LLMs are not very good at using tools correctly, with a success rate of only 30% to 60%. This is a problem because it means they won’t be able to learn and understand things as well as they could. To solve this problem, researchers have developed a new method called simulated trial and error (STE). STE helps LLMs learn how to use tools correctly by using three key mechanisms: trying out different ways of using the tool, imagining what might happen if you use the tool in a certain way, and remembering what worked well or didn’t work so well. This approach has been tested on a platform called ToolBench and has shown significant improvements in LLMs’ ability to learn and understand things. |
Keywords
* Artificial intelligence * Fine tuning * Gpt
