LLM System Evaluation using Inspect AI
vishnu vardhan (~vishnu1) |
Description:
Inspect AI is an open-source framework based on Python that simplifies the evaluation and validation of Large Language Model (LLM) systems. It offers a structured approach to measure the quality of outputs, ensuring the development of reliable AI applications. Evaluations in Inspect AI involve testing how well an LLM produces desired outputs, leading to more stable and dependable applications.
Evaluating LLM systems is crucial, especially when they are used for specific use cases that require customized evaluation strategies. Different applications may demand unique metrics and testing approaches to ensure that the models perform optimally in their intended contexts. Without thorough evaluations, it can be challenging to understand how different model versions and prompts affect specific use cases, making it difficult to maintain high standards and reliability.
The framework uses solvers, which handle evaluation tasks by generating initial responses, reasoning through steps, and refining responses. For instance, in a prompt optimization task, a solver might generate an initial answer, think through the logic (chain of thought), and then refine the response through self-critique.
Tools within Inspect AI enable models to perform specific tasks, enhancing their capabilities. These tools can be integrated into agent systems, which combine planning, memory, and tool usage to tackle complex tasks over multiple interactions. For example, an agent might use a tool to refine a generated response, ensuring it meets the required standards.
Scorers evaluate the success of solvers by comparing generated outputs to expected results. They can use text comparisons, model grading, and other methods to determine accuracy. In our prompt optimization task example, a scorer might use a model-graded QA method to assess the quality of responses, ensuring they align with the ideal answers.
Link to Inspect AI library: https://inspect.ai-safety-institute.org.uk
Prerequisites:
- Familiarity with Large Language Models (LLMs) and their applications.
- Some knowledge of AI model evaluation and testing processes.
Content URLs:
Speaker Info:
Vishnu Vardhan Lanka is a Associate Tech Lead at Kore.ai, actively engaged in the field of Generative AI and LLMs for the past one and a half years. His expertise lies in Machine Learning and NLP, with a growing proficiency in MLOps. Vishnu is committed to crafting AI solutions that are aligned and secure, with a focus on achieving tangible real-world impact.
Poorna Prudhvi Gurram is a Lead ML Engineer at EPAM with a strong expertise in machine learning, natural language processing, and backend engineering. He is dedicated to harnessing technology to address complex challenges and drive innovation. Beyond his professional pursuits, Prudhvi enjoys standup comedy, reading, traveling, and exploring diverse aspects of life, which fuels his creativity and broadens his perspective.