Summary of Assessing and Verifying Task Utility in Llm-powered Applications, by Negar Arabzadeh et al.
Assessing and Verifying Task Utility in LLM-Powered Applications
by Negar Arabzadeh, Siqing Huo, Nikhil Mehta, Qinqyun Wu, Chi Wang, Ahmed Awadallah, Charles L. A. Clarke, Julia Kiseleva
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses a critical gap in assessing the utility of Large Language Model (LLM) powered applications, which are increasingly used to facilitate collaboration among multiple agents and assist humans in daily tasks. The authors introduce AgentEval, a novel framework that simplifies the verification process by proposing criteria tailored to the application’s purpose. This enables a comprehensive assessment of an application’s utility against these criteria. The authors demonstrate the effectiveness and robustness of AgentEval using two open-source datasets: Math Problem solving and ALFWorld House-hold related tasks. To facilitate reproducibility, they provide public access to the data, code, and logs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well Language Model-powered tools work for people. These tools help groups of agents or humans do tasks together. The problem is that it’s hard to know if these tools really make things better. To fix this, the authors created a new way to check how good a tool is by giving it special criteria based on what the tool is meant to do. They tested this method using two different sets of problems: math and household tasks. The results show that their method works well and can be used again. |
Keywords
» Artificial intelligence » Language model » Large language model
