ToolTalk — Agent Tool by microsoft

by microsoft · Agent Tool · ★ 89

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About ToolTalk

:wrench: ToolTalk :speechballoon: :pagefacingup: Paper | :mailbox: Contact Introducing ToolTalk a benchmark for evaluating Tool LLMs in a conversational setting. Details ToolTalk is designed to evaluate tool-augmented LLMs as a chatbot, an increasingly popular paradigm for everyday users to harness the power of LLMs. ToolTalk contains a handcrafted dataset of 28 easy conversations and 50 hard conversations. We annotate these conversations to contain a ground truth usage of 28 unique tools belonging to 7 themed "plugins". Evaluation consists of prompting an LLM to predict the correct sequence of tools after every user utterance in a conversation. Thus, evaluating on a single conversation requires an LLM to correctly predict multiple sub-tasks. Predictions are compared against the ground truth to determine success for a single conversation. We evaluate two chatbots on ToolTalk powered by gpt-3.5-turbo-0613 and gpt-4-0613 implemented by using the chat completions API from OpenAI. | GPT-4

Quick Facts

Stars89
Forks15
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score34.9/100
Open Issues4
Last Updated2024-05-31
Created2023-10-10
Platformspython
Est. Tokens~19k

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Frequently Asked Questions

What is ToolTalk?

ToolTalk is Evaluating tool-augmented LLMs in conversation settings. It is categorized as a Agent Tool with 89 GitHub stars.

What programming language is ToolTalk written in?

ToolTalk is primarily written in Python.

How do I install or use ToolTalk?

You can find installation instructions and usage details in the ToolTalk GitHub repository at github.com/microsoft/ToolTalk. The project has 89 stars and 15 forks, indicating an active community.

What license does ToolTalk use?

ToolTalk is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to ToolTalk?

The top alternatives to ToolTalk on Agent Skills Hub include claude-skills-marketplace, tutor-skills, lenny-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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