Best Resources to Learn & Understand Evaluating LLMs

Author(s): Youssef Hosni

Originally published on Towards AI.

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.

As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level but also at the societal level for a better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives.

This article presents a comprehensive set of resources that will help you understand LLM evaluation starting from what to evaluate, where to evaluate, and how to evaluate.

1. Overview of LLM Evaluation Methods 1.1. Understanding LLM Evaluation and Benchmarks: A Complete Guide1.2. Decoding LLM Performance: A Guide to Evaluating LLM Applications1.3. A Survey on Evaluation of LLMs1.4. Evaluating and Debugging Generative AI

2. LLM Benchmarking2.1. The Definitive Guide to LLM Benchmarking

3. LLM Evaluation Methods3.1. BLEU at your own risk by Rachael Tatman3.2. Perplexity of fixed-length models3.3. HumanEval: Decoding the LLM Benchmark for Code Generation

4. Evaluating Chatbots 4.1 Chatbot Arena: Benchmarking LLMs in the Wild with Elo Ratings4.2 Chatbot Arena Leaderboard

5. Evaluating RAG Applications 5.1. Building and Evaluating Advanced RAG Applications

6. Automated Testing for LLMs6.1. Automated Testing for LLMOps

Most insights… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI