LLMs for Evaluating LLMs
Arthur's ML Engineers Max Cembalest & Rowan Cheung on LLMs evaluating other LLMs. Topics covered:
- Evolving Evaluation: LLMs require new evaluation methods to determine which models are best suited for which purposes.
- LLMs as Evaluators: LLMs are used to assess other LLMs, leveraging their human-like responses and contextual understanding.
- Biases and Risks: Understanding biases in LLM responses when judging other models is essential to ensure fair evaluations.
- Relevance and Context: LLMs can create testing datasets that better reflect real-world context, enhancing model applicability assessment.
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