Session brief
Best Practices for Evaluating Large Language Model Applications with llmeval: Niklas Nielsen
Overview
This talk introduces llmeval, a command-line tool designed to help teams ship reliable large language model (LLM) applications. It addresses the challenge of defining and measuring "good" in generative AI, which is crucial for evolving applications, changing prompts, or considering different models. llmeval provides a structured approach to testing and evaluating LLM outputs, enabling more confident development and deployment.
Who should watch
- AI Engineers
- Product Managers
- Builders working with LLM applications
- Teams struggling to define and measure quality in generative AI
- Developers looking for tools to test and iterate on LLM prompts and configurations
Key takeaways
- llmeval is a command-line tool that can be run locally to evaluate LLM applications.
- It supports a configurable system (based on Meta's Hydra) for defining tests, prompts, and metrics.
- The tool allows for flexible test criteria, acknowledging the fuzzy nature of LLM outputs, and can handle scenarios where a majority of cases should pass.
- llmeval can generate reports summarizing evaluation runs, highlighting passed and failed tests across different models.
- It supports testing for tool usage, ensuring LLMs can generate code or structured output that can be parsed.
- Model-based evaluation is presented as a method where one LLM grades the output of another, offering more nuanced feedback than simple pass/fail.
- The talk discusses pitfalls of model-based evaluation, such as biases towards a model's own output, and suggests bridging model-based and human feedback.
- An approach involving creating an "auto-human" agent that models prior human feedback is mentioned for generating AI-suggested feedback for review.
Notable quotes
*Without knowing what good means in a generative setting it's really really hard and risky to evolve your applications.*
*llmeval enables teams to ship reliable LLM products.*
*Model based evaluation is a setting where you have typically a larger model discriminate or kind of grade or be a judge over the output from another LLM.*
Unofficial community note. Prefer the recording for nuance.