Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis

TL;DR

This paper introduces LLM-Oasis, the largest dataset for training end-to-end factuality evaluators, addressing limitations of previous resources and challenging state-of-the-art LLMs in factuality assessment.

Contribution

The creation of LLM-Oasis, a large, task-agnostic dataset for training and benchmarking factuality evaluators, with human-validated claims and a challenging test set.

Findings

GPT-4o achieves up to 60% accuracy on the dataset

LLM-Oasis significantly challenges current LLMs in factuality evaluation

The dataset enables training more robust factuality evaluators

Abstract

After the introduction of Large Language Models (LLMs), there have been substantial improvements in the performance of Natural Language Generation (NLG) tasks, including Text Summarization and Machine Translation. However, LLMs still produce outputs containing hallucinations, that is, content not grounded in factual information. Therefore, developing methods to assess the factuality of LLMs has become urgent. Indeed, resources for factuality evaluation have recently emerged. Although challenging, these resources face one or more of the following limitations: (i) they are tailored to a specific task or domain; (ii) they are limited in size, thereby preventing the training of new factuality evaluators; (iii) they are designed for simpler verification tasks, such as claim verification. To address these issues, we introduce LLM-Oasis, to the best of our knowledge the largest resource for training end-to-end factuality evaluators. LLM-Oasis is constructed by extracting claims from Wikipedia, falsifying a subset of these claims, and generating pairs of factual and unfactual texts. We then rely on human annotators to both validate the quality of our dataset and to create a gold standard test set for benchmarking factuality evaluation systems. Our experiments demonstrate that LLM-Oasis presents a significant challenge for state-of-the-art LLMs, with GPT-4o achieving up to 60% accuracy in our proposed end-to-end factuality evaluation task, highlighting its potential to drive future research in the field.