Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280

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Dec 23, 2024

In this insightful discussion, Jineet Doshi, an award-winning AI lead with over seven years at Intuit, dives deep into the complexities of evaluating generative AI systems. He emphasizes the importance of holistic evaluation to foster trust and the unique challenges posed by large language models. Jineet explores diverse evaluation methods, from classic NLP techniques to innovative strategies like red teaming. He also tackles the financial nuances of generative AI and the balance between human insight and automated feedback for robust assessments.

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INSIGHT

Traditional NLP Techniques for LLM Evaluation

Traditional NLP techniques can be applied to LLM evaluation by using multiple-choice questions or text similarity.
However, these methods have limitations in evaluating open-ended tasks and can be sensitive to the choice of embedding models.

ADVICE

Using Benchmarks for LLM Evaluation

Use benchmarks to evaluate LLMs across various factors like knowledge, reasoning, and toxicity.
Be mindful of benchmark limitations, data leakage, and the need for custom benchmarks for specific use cases.

ADVICE

Human-Based Evaluation Approaches

Human evaluation is considered the gold standard but can be expensive and difficult to scale.
Use well-defined criteria, pairwise comparisons, and specialized red teams for effective human evaluation.

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