Researchers introduce SCALEEVAL, a framework utilizing multiple LLM agents engaging in agent-debate to evaluate LLMs as responders. It reduces reliance on costly human annotation, balancing efficiency and human judgment for accurate assessments. It exposes effectiveness and limitations of LLMs in varied scenarios, advancing scalable evaluation methods crucial for expanding LLM applications.
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Can Large Language Models be Trusted for Evaluation? Meet SCALEEVAL: An Agent-Debate-Assisted Meta-Evaluation Framework that Leverages the Capabilities of Multiple Communicative LLM Agents
Despite the utility of large language models (LLMs) across various tasks and scenarios, researchers need help to evaluate LLMs properly in different situations. They urgently need better ways to test how well LLMs can evaluate things in all situations, especially when users define new scenarios.
SCALEEVAL: A Practical Solution
Researchers have introduced SCALEEVAL, a scalable meta-evaluation framework utilizing agent-debate assistance to assess LLMs as evaluators. This proposal addresses the inefficiencies of conventional, resource-intensive meta-evaluation methods, crucial as LLM usage grows. The study not only validates the reliability of SCALEEVAL but also illuminates the capabilities and limitations of LLMs in diverse scenarios. This work contributes to advancing scalable solutions for evaluating LLMs, vital for their expanding applications.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Can Large Language Models be Trusted for Evaluation? Meet SCALEEVAL: An Agent-Debate-Assisted Meta-Evaluation Framework that Leverages the Capabilities of Multiple Communicative LLM Agents
- MarkTechPost
- Twitter – @itinaicom
