From the course: AI Orchestration: Validation and User Feedback and Performance Metrics

Unlock this course with a free trial

Join today to access over 25,300 courses taught by industry experts.

Evaluating large language models (LLMs)

Evaluating large language models (LLMs)

From the course: AI Orchestration: Validation and User Feedback and Performance Metrics

Start my 1-month free trial Buy for my team

Evaluating large language models (LLMs)

“

- [Instructor] We've discussed the metrics that we'd use to evaluate traditional machine-learning models. How would we evaluate large language models? But first, let's talk about why evaluation of LLMs is even necessary. LLMs are increasing the used in applications that directly impact users, such as chat bots, content creation tools, and even medical diagnosis systems. This means it's very important to ensure that the outputs of these models are accurate, relevant to what the user wants, and safe for end users. This is especially important because LLMs can be used to influence opinions, disseminate information, and even spread propaganda. We've already discussed that LLMs are prone to hallucinations. They can sometimes generate incorrect or nonsensical outputs, and they can exhibit biases present in the vast data sets that they are trained on. Evaluating LLMs helps detect these issues, such as hallucinations, biases, or inconsistencies in responses. Evaluation also helps align model…

Contents