Summary of Agent-as-a-judge: Evaluate Agents with Agents, by Mingchen Zhuge et al.
Agent-as-a-Judge: Evaluate Agents with Agents
by Mingchen Zhuge, Changsheng Zhao, Dylan Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, Jürgen Schmidhuber
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper introduces the Agent-as-a-Judge framework, an extension of the LLM-as-a-Judge framework, which uses agentic systems to evaluate agentic systems. This approach enables intermediate feedback during the task-solving process, addressing limitations in current evaluation techniques. The framework is applied to code generation and benchmarked against three popular agentic systems using the DevAI dataset, a new benchmark featuring 55 realistic automated AI development tasks with rich manual annotations. The results show that Agent-as-a-Judge outperforms LLM-as-a-Judge and is as reliable as human evaluation, marking a step forward for modern agentic systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test artificial intelligence systems that make decisions and take actions. It uses these systems to evaluate themselves, giving feedback at different stages of the process. This helps solve some problems with current testing methods. The researchers apply this approach to a specific task called code generation and use a new dataset with 55 tasks and detailed annotations. They compare their method to others and find it works better and is just as good as human evaluators. |
