Summary of Automated Test Generation to Evaluate Tool-augmented Llms As Conversational Ai Agents, by Samuel Arcadinho et al.
Automated test generation to evaluate tool-augmented LLMs as conversational AI agents
by Samuel Arcadinho, David Aparicio, Mariana Almeida
First submitted to arxiv on: 24 Sep 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 A novel test generation pipeline is proposed to evaluate Large Language Models (LLMs) as conversational AI agents, tackling the challenge of evaluating their ability to have realistic conversations, follow procedures, and call appropriate functions. The framework uses LLMs to generate diverse tests grounded on user-defined procedures, limiting hallucination through intermediate graphs and enforcing high coverage of possible conversations. A manually curated dataset, ALMITA, is also presented for evaluating AI agents in customer support. Results show that while tool-augmented LLMs excel in single interactions, they struggle with complete conversations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are designed to create AI agents that can have realistic conversations, follow procedures, and call the right functions. To test these abilities, a new method is developed. It uses LLMs to generate different tests based on user-defined procedures. This helps prevent the tests from including information that isn’t related to the procedure. The goal is to cover all possible conversations. A dataset called ALMITA is also created to evaluate AI agents in customer support. The results show that while LLMs are good at handling single interactions, they struggle with longer conversations. |
Keywords
» Artificial intelligence » Hallucination
