Summary of Towards Better Human-agent Alignment: Assessing Task Utility in Llm-powered Applications, by Negar Arabzadeh and Julia Kiseleva and Qingyun Wu and Chi Wang and Ahmed Awadallah and Victor Dibia and Adam Fourney and Charles Clarke
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
by Negar Arabzadeh, Julia Kiseleva, Qingyun Wu, Chi Wang, Ahmed Awadallah, Victor Dibia, Adam Fourney, Charles Clarke
First submitted to arxiv on: 14 Feb 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 the challenge of verifying the utility of Large Language Model (LLM) powered applications that assist humans in their daily tasks. The authors highlight the need for methods to ensure alignment between an application’s functionality and end-user needs, as current developments have led to a surge in such applications without adequate assessment of their impact on user experience and task execution efficiency. To tackle this issue, they introduce AgentEval, a novel framework that simplifies the utility verification process by automatically proposing a set of criteria tailored to the unique purpose of any given application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making sure that computer programs powered by Large Language Models actually help people and make their lives easier. Right now, there are many applications being developed without being tested properly to see if they really work well for users. The authors created a new tool called AgentEval that helps figure out what makes an application useful or not. |
Keywords
» Artificial intelligence » Alignment » Large language model
