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ToolTalk: Benchmarking Tool-Augmented LLMs in Conversational AIby@botbeat
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ToolTalk: Benchmarking Tool-Augmented LLMs in Conversational AI

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ToolTalk introduces a benchmark for assessing tool-augmented LLMs in conversational AI, showcasing its methodology and evaluation of GPT-3.5 and GPT-4. The analysis identifies error categories and hints at future directions for AI research in tool usage and dataset expansion.
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Authors:

(1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {[email protected]};

(2) Richard Shin, Microsoft Corporation {[email protected]}.

Abstract and Intro

Dataset Design

Evaluation Methodology

Experiments and Analysis

Related Work

Conclusion, Reproducibility, and References

A. Complete list of tools

B. Scenario Prompt

C. Unrealistic Queries

D. Nuances comparing prior work

6 CONCLUSION

We present ToolTalk, a new benchmark for evaluating tool-augmented LLMs in a conversational setting. Our benchmark emphasizes complex orchestration of multiple tools in a conversational setting. We provide simulated implementations of all tools, allowing for a fully automated evaluation where the LLM can decide which tools to further invoke based on the results of prior tool calls. Finally, we also introduce a unique form of evaluating correctness that takes into account unique aspects of individual tools and whether a tool usage system produces incorrect actions. We evaluate GPT-3.5 and GPT-4 using our dataset and methodology and analyze their errors, finding three major categories: premature tool calls, faulty reasoning, and incorrect invocations of the correct tool. In the future, we hope to expand the scope of this dataset to more conversations and simulate even more, diverse plugins. We also hope to see future research look into how to better redesign existing API interfaces for LLMs.

7 REPRODUCIBILITY

We make ToolTalk more widely available by releasing it on github[5]. We include the exact versions of GPT-3.5 (gpt-3.5-turbo-0613) and GPT-4 (gpt-4-0613) available through the OpenAI API to be able to reproduce our results after release. We include the prompt used to generate our scenarios in Appendix B. We include information on system prompts and our application of OpenAI’s Chat completions API in Section 4.1.

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This paper is available on arxiv under CC 4.0 license.


[5] https://github.com/microsoft/ToolTalk