bibtext: @inproceedings{
  shafayat2024multifact,
  title={Multi-{FA}ct: Assessing Factuality of Multilingual {LLM}s using {FA}ctScore},
  author={Sheikh Shafayat and Eunsu Kim and Juhyun Oh and Alice Oh},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=lkrH6ovzsj}
  }
abstract: Evaluating the factuality of long-form large language model (LLM)-generated text is an important challenge. Recently there has been a surge of interest in factuality evaluation for English, but little is known about the factuality evaluation of multilingual LLMs, specially when it comes to long-form generation.
  This paper systematically evaluates multilingual LLMs' factual accuracy across languages and geographic regions.
  We introduce a simple pipeline for multilingual factuality evaluation, by applying FActScore \citep{min2023factscore} for diverse languages. In addition to evaluating multilingual factual generation, we evaluate the factual accuracy of long-form text generation in topics that reflect regional diversity. We also examine the feasibility of running the FActScore pipeline using non-English Wikipedia and provide comprehensive guidelines on multilingual factual evaluation for regionally diverse topics.
title: Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore
authors: Sheikh Shafayat Eunsu Kim Juhyun Oh Alice Oh