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Evaluating Large Language Models (LLMs) Outputs

Evaluating Large Language Models (LLMs) Outputs

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Overview:

This course delves into evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation. It emphasizes practical applications, the integration of human judgment alongside automatic methods, and prepares learners for future trends in AI evaluation across various media including text, images, and audio. This comprehensive approach ensures learners are equipped to assess LLMs effectively, enhancing business strategies and innovation.

Main Outcome and Takeaways: Navigate the complexities of evaluating generative AI models through Google Cloud's Vertex AI, mastering evaluation tools and services for optimizing LLM application development.

  • Understand Evaluation Challenges: Grasp the challenges in evaluating generative AI models, including data scarcity, metric inadequacies, and decision space complexities. (Knowledge)
  • Discover Vertex AI Services: Learn about the specific evaluation services offered by Google Cloud Vertex AI, such as Automatic Metrics and AutoSxS, and their roles in assessing model performance. (Comprehension)
  • Optimize Model Selection: Gain the ability to use these tools to select the most suitable model for your applications, enhancing performance and efficiency. (Application)
  • Future-Proof Your Skills: Prepare for the future by understanding how evolving evaluation tools and services can impact the development and deployment of large language models. (Analysis)

Skills Included:

  • Grasp generative AI model evaluation complexities.
  • Learn using Vertex AI's evaluation services.
  • Develop skills in choosing the right evaluation model.
  • Stay ahead with evolving evaluation techniques.

Case Studies and Examples:

 

Duration: 60 Minutes

Level: Beginner to Intermediate

Audience:

  • AI Product Managers who are looking to enhance product offerings with optimized LLM applications.
  • Data Scientists interested in advanced methodologies for AI model evaluation.
  • AI Ethicists and Policy Makers focused on the responsible deployment of AI technologies.
  • Academic Researchers studying generative AI's impact across different domains.

Proof Of Learning: The course will include In Video Questions, as well as Practice Quizzes and a Graded Assessment.

Learning Objectives (After this course, Learners will be able to…) 

  • LO1 - Grasp LLM Evaluation Basics: Understand the fundamentals of Large Language Models, including current evaluation methods and access to Vertex AI's evaluation models.
  • LO2 - Dive into Vertex AI Evaluation: Gain in-depth knowledge of using Vertex AI's Automatic Metrics and AutoSxS for LLM evaluation.
  • LO3 - Explore Future Evaluation Trends: Learn about upcoming trends in generative AI evaluation, encompassing text, image, and audio models, and the importance of human evaluation.

Outline:

Lesson 1: Basics of Large Language Models Evaluation Methods

In this lesson, we will discuss the concept of Large Language Models, the benefits and challenges of current LLM Evaluation methods, and how to access Vertex AI’s off the shelf LLM evaluation models.

Learning Items
 Learning Item Title Aligned LO High level Description Est. Time
Introductory Video Introduction and Welcome - A brief description of the Course Structure and Learning Objectives 3 mins
L1V1 Introduction to LLMs and their evaluation methods LO1 In this video, you will learn what are large language models, how they are different from traditional natural language processing (NLP) models, and why it is important to have reliable methods for evaluating them. 5 mins
L1V2 Benefits and Challenges of LLM Evaluation Methods LO1 In this video, you will learn about the benefits of current LLM evaluation methods and the challenges that future methods must address. 7 mins
L1V3 LLM Evaluation on Vertex AI LO1 In this video, you will learn how to navigate Google Cloud Vertex AI in order to access its off the shelf LLM Evaluation models. 5 mins
Reading 1

Evaluating Large Language Models (LLMs): A Standard Set of Metrics for Accurate Assessment

 LO1 Large Language Models (LLMs) are AI models trained on vast text datasets for tasks like translation, answering questions, and generating text. Their evaluation is crucial for ensuring effective performance and high-quality output, particularly in decision-making or informational applications. 10 mins

Link to Reading/ Video Script: https://www.linkedin.com/pulse/evaluating-large-language-models-llms-standard-set-metrics-biswas-ecjlc/

Lesson 2: LLM Evaluation on Vertex AI

In this lesson, we will have a deep dive into Automatic Metrics and AutoSxS, two LLM evaluation models available on Google Cloud Vertex AI.

Learning Items
 Learning Item Title Aligned LO High level Description Est. Time
L2V1 Automatic Metrics LO2 In this video, you will learn what automatic metrics are available on Vertex AI for evaluating the output of LLMs. 5 mins
L2V2 Automatic Metrics Demo LO2 In this video, you will see a demo of evaluating the output of an LLM using Automatic Metrics on Vertex AI. 7 mins
L2V3 AutoSxS LO2 In this video, you will learn what AutoSxS on Vertex AI is and how to use it for evaluating and comparing the output of multiple LLMs. 5 mins
L2V4 AutoSxS Demo LO2 In this video, you will see a demo of evaluating the output of two LLMs for the same task using AutoSxS on Vertex AI. 7 mins
Reading 2

Google Generative AI Evaluation Service

 LO2 This reading discusses insights, features, and functionalities of Google's Vertex AI for evaluating generative AI models. 10 mins

Link to Reading/ Video Script: Google Generative AI Evaluation Service | by Sascha Heyer | Medium

Lesson 3: The Future of Generative AI Evaluation Models

In this lesson, we will introduce other text-based evaluation models, discuss evaluation techniques for non-text Gen AI models such as image, sound and audio, and point out to the importance of including human evaluation to any automatic evaluation model.

Learning Items
 Learning Item Title Aligned LO High level Description Est. Time
L3V1 Text-based Evaluation Models – Part 1 LO3 In this video, you will learn about some text-based evaluation models. 5 mins
L3V2 Text-based Evaluation Models – Part 2 LO3 In this video, you will learn about more text-based evaluation models. 5 mins
L3V3 Evaluation of non-text Generative AI Models LO3 In this video, you will learn about available evaluation techniques for non-text Gen AI models such as image, sound and audio. 5 mins
L3V4 Final Notes: Importance of Human Evaluation LO3 In this video, you will be presented with a summary of the course, and some remarks on the importance of including human evaluation to any automatic evaluation model. 6 mins
Reading 3

What are the most effective ways to evaluate generative AI models for image generation?

 LO3 The article discusses methods for evaluating image-generating AI models, highlighting the importance of combining human judgment with various metrics to assess quality, diversity, and relevance of the images produced. 10 mins

Link to Reading/ Video Script: https://www.linkedin.com/advice/1/what-most-effective-ways-evaluate-generative

Conclusions and Takeaways:

Recap of key concepts, strategies for leveraging LLM evaluation, and thoughts on future Gen AI trends.

Proof of Learning:

In-video questions in each video for interactive learning.

Course Evaluation comprising 10-15 Multiple Choice Questions aligned with the learning objectives.

Course Continuous Learning Journey Statement:

Encouraging ongoing learning and adaptation in the dynamic field of AI, with recommendations for advanced study and resources.

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