Evaluating Language Models: Metrics and Data Challenges

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This chapter examines the intricate evaluation of large language models across diverse languages and domains, emphasizing the need for aligned metrics and addressing biases in assessments. It also highlights the significance of understanding user query distribution, effective data processing, and the role of synthetic data in overcoming the challenges of messy production environments.

How do you systematically measure, optimize, and improve the performance of LLM applications (like those powered by RAG or tool use)? Ragas is an open source effort that has been trying to answer this question comprehensively, and they are promoting a “Metrics Driven Development” approach. Shahul from Ragas joins us to discuss Ragas in this episode, and we dig into specific metrics, the difference between benchmarking models and evaluating LLM apps, generating synthetic test data and more.

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