A Practical Guide for Evaluating LLMs and LLM-Reliant Systems
This addresses the problem of meaningful evaluation for practitioners and developers working with LLMs, though it appears incremental as it builds on existing evaluation concepts.
The paper tackles the challenge of evaluating large language models (LLM) and LLM-reliant systems in real-world scenarios, presenting a practical framework for curating datasets, selecting metrics, and employing methodologies that align with development and deployment needs.
Recent advances in generative AI have led to remarkable interest in using systems that rely on large language models (LLMs) for practical applications. However, meaningful evaluation of these systems in real-world scenarios comes with a distinct set of challenges, which are not well-addressed by synthetic benchmarks and de-facto metrics that are often seen in the literature. We present a practical evaluation framework which outlines how to proactively curate representative datasets, select meaningful evaluation metrics, and employ meaningful evaluation methodologies that integrate well with practical development and deployment of LLM-reliant systems that must adhere to real-world requirements and meet user-facing needs.