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How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269
Oct 18, 2024
Gideon Mendels, CEO and co-founder of Comet, dives into the intricate world of testing and evaluating LLMs. He discusses the hybrid approach required for these applications, merging machine learning with software engineering best practices. Topics include innovative methods for evaluating LLMs beyond traditional metrics, the challenge of unit testing with deterministic assertions, and the importance of experiment tracking in ensuring reproducibility. Gideon also highlights the role of user interaction analysis in enhancing LLM applications' performance.
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Quick takeaways
- Effective evaluation metrics for LLMs require a shift from traditional accuracy-focused measures to task-specific metrics tailored for nuanced outputs.
- Human labeling plays a critical role in refining LLM performance, though it can be costly, highlighting the necessity for quality labeled datasets.
Deep dives
The Shift from Traditional ML to Evaluation in LLMs
Evaluation metrics for machine learning models, particularly Large Language Models (LLMs), differ significantly from traditional metrics. Traditional machine learning typically evaluates models based on accuracy and F1 scores, which are straightforward to compute with labeled datasets. However, in the context of LLMs, metrics like perplexity and various heuristic distances become relevant, as the output may not conform to a strict string format yet still convey the same meaning. Understanding the task-specific nature of evaluation is crucial, as deploying LLMs often introduces unpredictability in their responses, necessitating different evaluation approaches than those used during training.
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