Summary Tool Documentation Enables Zero-Shot Tool-Usage arxiv.org
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One Line
Large language models can achieve comparable performance without the need for demonstrations by utilizing tool documentation instead.
Slides
Slide Presentation (7 slides)
Key Points
- Large language models (LLMs) can rely on tool documentation instead of demonstrations to use new tools.
- Tool documentation enables zero-shot tool usage for LLMs, reducing the reliance on few-shot demos.
- The LLM Cloud CLI benchmark consists of 200 tools and can be used to explore real-world use cases.
- Tool documentation can achieve comparable performance to using a small number of demonstrations.
- LLMs have the capacity to comprehend and combine new tools with documentation, enabling automatic knowledge discovery.
Summaries
20 word summary
Large language models (LLMs) can use tool documentation instead of demonstrations, achieving comparable performance without the need for few-shot demos.
40 word summary
Large language models (LLMs) can rely on tool documentation instead of demonstrations to use tools, sidestepping the need for few-shot demos. Tool documentation enables zero-shot tool usage, allowing models to achieve comparable performance to using a small number of demonstrations
500 word summary
Large language models (LLMs) are typically taught to use new tools through demonstrations, but this approach has limitations. Demonstrations can be hard to acquire and may result in biased usage. Additionally, there is no established protocol for selecting the number and type
Documentation enables zero-shot tool-usage for Language and Learning Models (LLMs). LLMs can rely solely on tool documentation instead of demos to use tools. Including docs is an effective way to sidestep the need for few-shot demos. Ex
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VisProg, a tool for generating documentation for modules, is able to enable zero-shot tool usage without relying on demonstrations. To explore real-world use cases with a large number of tools, a new benchmark called the LLM Cloud CLI consisting of 200
Tool documentation can enable zero-shot tool usage, allowing models to achieve comparable performance to using a small number of demonstrations. The model's performance is sensitive to the number of demos used, but with tool docs, the reliance on demos can be reduced. By
The document discusses the effectiveness of tool documentation in enabling zero-shot tool usage with Language Models (LLMs). It highlights the LLM's capacity to comprehend and combine new tools with documentation, demonstrating its potential for automatic knowledge discovery. The impact of documentation quality
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To deploy a machine learning model saved locally to the cloud via the SDK command line, use the command "gcloud ai-platform versions create VERSION -model MODEL -origin gs://LOC/model.pt". To obtain a transcript of a local video using the cloud