MLOps.community LLM Evaluation with Arize AI's Aparna Dhinakaran // #210
Feb 9, 2024
The podcast discusses the complexities of Language Model evaluation, the use of open-source versus private models, and the urgency of getting models into production. It also explores the challenges of evaluating LLM outcomes and highlights the importance of prompt engineering. Additionally, it emphasizes the need to quickly get ML models into production for identifying bottlenecks and setting up metrics.
AI Snips
Chapters
Transcript
Episode notes
LLMs as Judges for Evaluation
- Evaluating LLM outputs is more complex than traditional ML metrics like classification or regression metrics.
- Using LLMs as judges to evaluate other LLM outputs enables assessing correctness, hallucination, and toxicity without ground truth.
Use LLM Judge in Offline and Online
- Use LLM as judge evaluation both offline and online to balance speed and accuracy.
- Block low-confidence or hallucinated answers from reaching end users to improve quality.
Evaluate Retrieval for LLM Accuracy
- Evaluating retrieval is crucial since LLM answers depend on relevant retrieved context.
- Retrieval performance varies significantly across models and prompt design, impacting hallucination risk.