Year: 2024
Type: article
Abstract: Evaluating large language models (LLMs) is essential to understanding their performance, biases, and limitations.This guide outlines key evaluation methods, including automated metrics like perplexity, BLEU, and ROUGE, alongside human assessments for open-ended tasks.It covers core attributes such as bias, factual accuracy, and robustness, while also discussing benchmark datasets and tools like OpenAI Evals and HELM.The paper addresses challenges in LLM evaluation, including consistency and data leakage, and explores emerging trends like multi-modal evaluation and adversarial testing.This framework aims to help improve the reliability and ethical deployment of LLMs.
Author: Aleksandr Timashov
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404149281