Evaluating large language models (LLMs) is often unreliable due to inherent non-determinism in the models themselves and the evaluation process. Factors like token sampling temperature, the LLM used as a judge, and changes to the dataset can cause significant score fluctuations, making it difficult to distinguish genuine model regressions from measurement noise. To improve reliability, developers should treat LLM evaluations like any other flaky test suite by controlling randomness through fixed seeds and temperature settings, versioning datasets like source code, and calibrating LLM judges against human-labeled data to establish a baseline agreement. AI
IMPACT Highlights the need for more robust and reproducible evaluation methods for LLMs to ensure reliable progress tracking.
RANK_REASON The item discusses best practices for evaluating LLMs, framing it as a commentary on current methodologies rather than a new release or research finding.
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