The general purpose evaluation metrics measure the overlap between the model response and the reference answer. Fine-tuned LLMs are used to yield evaluations assessing the correctness of a model response given a reference answer. RAG specific evaluation metrics evaluate the retrieval and generation steps in an RAG application using labeled data. The article also provides information on how to get started with these evaluation metrics, either independently or through a platform called Parea, which offers dedicated solutions to evaluate, monitor, and improve the performance of LLM & RAG applications.
Key takeaways:
- The article discusses various evaluation metrics for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, which rely on ground truth annotations/reference answers to assess the model response.
- General purpose evaluation metrics using foundational models measure the overlap between the model response and the reference answer. The most predictive metric for the correctness of the model response is to use another LLM for grading it.
- Fine-tuned LLMs are used as general purpose evaluation metrics. Examples include Prometheus, CritiqueLLM, and InstructScore, which all fine-tune LLMs to yield evaluations assessing the correctness of a model response given a reference answer.
- RAG specific evaluation metrics evaluate the retrieval and generation steps in an RAG application using labeled data. Examples include Percent Target Supported by Context and ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems.