Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore

• URL: http://arxiv.org/abs/2402.18045v3
• Date: Thu, 03 Oct 2024 14:44:44 GMT
• Title: Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore
• Authors: Sheikh Shafayat, Eunsu Kim, Juhyun Oh, Alice Oh,
• Abstract summary: We introduce a simple pipeline for multilingual factuality evaluation, by applying FActScore for diverse languages. We evaluate the factual accuracy of long-form text generation in topics that reflect regional diversity.
• Score: 14.91669562846729
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (Min et al., 2023) 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.

Related papers

• PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts [79.84059473102778]
  PolyMath is a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels. Our benchmark ensures difficulty comprehensiveness, language diversity, and high-quality translation.
  arXiv  Detail & Related papers  (2025-04-25T15:39:04Z)
• MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation [60.52580061637301]
  MMLU-ProX is a comprehensive benchmark covering 13 typologically diverse languages with approximately 11,829 questions per language. We evaluate 25 state-of-the-art large language models (LLMs) using 5-shot chain-of-thought (CoT) and zero-shot prompting strategies, analyzing their performance across linguistic and cultural boundaries. Our experiments reveal consistent performance degradation from high-resource languages to lower-resource ones, with the best models achieving over 70% accuracy on English but dropping to around 40% for languages like Swahili.
  arXiv  Detail & Related papers  (2025-03-13T15:59:20Z)
• INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge [36.234295907476515]
  The development of functional large language models (LLM) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. In this work, we construct an evaluation suite of 197,243 QA pairs from local exam sources to measure the capabilities of multilingual LLMs in a variety of regional contexts.
  arXiv  Detail & Related papers  (2024-11-29T16:03:14Z)
• Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs [36.30321941154582]
  Hercule is a cross-lingual evaluation model that learns to assign scores to responses based on easily available reference answers in English. This study is the first comprehensive examination of cross-lingual evaluation using LLMs, presenting a scalable and effective approach for multilingual assessment.
  arXiv  Detail & Related papers  (2024-10-17T09:45:32Z)
• Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs [31.893686987768742]
  Language models are inconsistent in their ability to answer the same factual question across languages. We explore multilingual factual knowledge through two aspects: the model's ability to answer a query consistently across languages, and the ability to ''store'' answers in a shared representation for several languages.
  arXiv  Detail & Related papers  (2024-08-20T08:38:30Z)
• Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models [22.859955360764275]
  We introduce the MultiLingual Needle-in-a-Haystack (MLNeedle) test to assess a model's ability to retrieve relevant information. We evaluate four state-of-the-art large language models on MLNeedle.
  arXiv  Detail & Related papers  (2024-08-19T17:02:06Z)
• Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models [7.615938028813914]
  With Retrieval Augmented Generation (RAG), Large Language Models (LLMs) are playing a pivotal role in information search. We studied LLM's linguistic preference in a RAG-based information search setting. We found that LLMs displayed systemic bias towards information in the same language as the query language in both information retrieval and answer generation.
  arXiv  Detail & Related papers  (2024-07-07T21:26:36Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We find that Llama Instruct and Mistral models exhibit high degrees of language confusion. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• An Analysis of Multilingual FActScore [45.48784238480873]
  FActScore has gained popularity as a metric to estimate the factuality of long-form texts generated by Large Language Models (LLMs) in English. This paper studies the limitations of each component in the four-component pipeline of FActScore in the multilingual setting.
  arXiv  Detail & Related papers  (2024-06-20T18:09:40Z)
• Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval [62.82448161570428]
  This dataset is designed to investigate fairness in a multilingual information retrieval context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages. It offers rich demographic information associated with its documents, facilitating the study of demographic bias.
  arXiv  Detail & Related papers  (2023-11-03T12:29:11Z)
• CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages [86.90220551111096]
  Training datasets for large language models (LLMs) are often not fully disclosed. We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages.
  arXiv  Detail & Related papers  (2023-09-17T23:49:10Z)
• X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models [103.75890012041366]
  Language models (LMs) have proven surprisingly successful at capturing factual knowledge. However, studies on LMs' factual representation ability have almost invariably been performed on English. We create a benchmark of cloze-style probes for 23 typologically diverse languages.
  arXiv  Detail & Related papers  (2020-10-13T05:29:56Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.