Year: 2025
Type: conference-paper
Abstract: Improvements in large language models have led to increasing optimism that they can serve as reliable evaluators of natural language generation outputs.In this paper, we challenge this optimism in regards to factuality evaluation.We re-evaluate five state-of-the-art factuality metrics on a collection of 11 datasets for summarization, retrieval-augmented generation, and question answering.We find that these evaluators are inconsistent with each other and often misestimate the factual accuracy of NLG systems, both of which can lead to a variety of pitfalls.We further show that these metrics exhibit biases against highly paraphrased outputs and outputs that draw upon faraway parts of the source documents.We urge users of factuality metrics to proceed with caution and manually validate the reliability of these metrics in their domain of interest.
Authors: Ameya Godbole, Robin Jia
Institution: University of Southern California
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W4412887776