Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280
Dec 23, 2024
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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.
Establishing a holistic evaluation framework for generative AI systems is essential to assess their performance across diverse use cases effectively.
Using LLMs as evaluators offers a scalable solution for assessment but necessitates rigorous validation to mitigate bias and ensure reliability.
Deep dives
Importance of Holistic Evaluation in AI Systems
Evaluating generative AI systems is crucial for building trustworthy machine learning models. The conversation emphasizes that evaluation should not solely focus on individual model outputs but also consider the system as a whole, including how models interact and retrieve information. This holistic approach helps identify whether the AI system meets the desired performance metrics across various use cases. The speaker highlights that as generative models can produce diverse outputs, establishing comprehensive evaluation strategies becomes a necessity for enhancing AI systems.
Challenges of Evaluating LLMs
The evaluation of large language models (LLMs) presents significant challenges due to their capability to generate open-ended outputs. Unlike traditional machine learning models that produce specific results based on well-defined tasks, LLMs can produce countless valid outputs, complicating how their performance is measured. Consequently, developing standardized metrics that accommodate the varied nature of LLM outputs remains an unresolved issue. The speaker draws parallels between evaluating LLMs and measuring human intelligence, indicating that both domains struggle with creating definitive assessment methods.
Types of Evaluation Techniques
The discussion outlines several categories of evaluation techniques applicable to generative AI systems, starting with traditional NLP approaches. By adapting conventional methods like multiple-choice questions or text similarity metrics, evaluators can impose some structure on LLM outputs, making them easier to assess. However, this approach has limitations since it often cannot capture the full scope of open-ended tasks. Additionally, human evaluations play a vital role, as they provide qualitative insights, although they tend to be expensive and less scalable.
Model-Based Evaluation Strategies
The conversation also delves into the emerging strategy of using LLMs as evaluators, where these models assess outputs based on their own learned criteria. This method offers a scalable alternative to human evaluations, but it raises concerns regarding bias and the quality of the selected judging model. The speaker mentions the necessity of validating the evaluator's performance against a reliable benchmark to ensure its effectiveness. Exploring techniques like employing a group of LLMs to judge outputs collectively can further enhance the reliability of model-based evaluations.
Jineet Doshi is an award-winning Scientist, Machine Learning Engineer, and Leader at Intuit with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine-learning models from design to production across various domains which have impacted 100 million customers and significantly improved business metrics, leading to millions of dollars of impact.
Holistic Evaluation of Generative AI Systems // MLOps Podcast #280 with Jineet Doshi, Staff AI Scientist or AI Lead at Intuit.
// Abstract
Evaluating LLMs is essential in establishing trust before deploying them to production. Even post deployment, evaluation is essential to ensure LLM outputs meet expectations, making it a foundational part of LLMOps. However, evaluating LLMs remains an open problem. Unlike traditional machine learning models, LLMs can perform a wide variety of tasks such as writing poems, Q&A, summarization etc. This leads to the question how do you evaluate a system with such broad intelligence capabilities? This talk covers the various approaches for evaluating LLMs such as classic NLP techniques, red teaming and newer ones like using LLMs as a judge, along with the pros and cons of each. The talk includes evaluation of complex GenAI systems like RAG and Agents. It also covers evaluating LLMs for safety and security and the need to have a holistic approach for evaluating these very capable models.
// Bio
Jineet Doshi is an award winning AI Lead and Engineer with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine learning models from design to production across various domains, which have impacted millions of customers and have significantly improved business metrics, leading to millions of dollars of impact. He is currently an AI Lead at Intuit where he is one of the architects and developers of their Generative AI platform, which is serving Generative AI experiences for more than 100 million customers around the world.
Jineet is also a guest lecturer at Stanford University as part of their building LLM Applications class. He is on the Advisory Board of University of San Francisco’s AI Program. He holds multiple patents in the field, is on the steering committee of MLOps World Conference and has also co chaired workshops at top AI conferences like KDD. He holds a Masters degree from Carnegie Mellon university.
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https://shop.mlops.community/
// Related Links
Website: https://www.intuit.com/
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