Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking

• URL: http://arxiv.org/abs/2502.15419v1
• Date: Fri, 21 Feb 2025 12:38:26 GMT
• Title: Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking
• Authors: Yi-Ling Chung, Aurora Cobo, Pablo Serna,
• Abstract summary: MultiSynFact is the first large-scale multilingual fact-checking dataset containing 2.2M claim-source pairs.<n>Our dataset generation pipeline leverages Large Language Models (LLMs), integrating external knowledge from Wikipedia.<n>We open-source a user-friendly framework to facilitate further research in multilingual fact-checking and dataset generation.
• Score: 2.321323878201932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust automatic fact-checking systems have the potential to combat online misinformation at scale. However, most existing research primarily focuses on English. In this paper, we introduce MultiSynFact, the first large-scale multilingual fact-checking dataset containing 2.2M claim-source pairs designed to support Spanish, German, English, and other low-resource languages. Our dataset generation pipeline leverages Large Language Models (LLMs), integrating external knowledge from Wikipedia and incorporating rigorous claim validation steps to ensure data quality. We evaluate the effectiveness of MultiSynFact across multiple models and experimental settings. Additionally, we open-source a user-friendly framework to facilitate further research in multilingual fact-checking and dataset generation.

Related papers

• Enhancing Multilingual Language Models for Code-Switched Input Data [0.0]
  This research investigates if pre-training Multilingual BERT (mBERT) on code-switched datasets improves the model's performance on critical NLP tasks. We use a dataset of Spanglish tweets for pre-training and evaluate the pre-trained model against a baseline model. Our findings show that our pre-trained mBERT model outperforms or matches the baseline model in the given tasks, with the most significant improvements seen for parts of speech tagging.
  arXiv  Detail & Related papers  (2025-03-11T02:49:41Z)
• UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset [69.33424532827608]
  Open-source large language models (LLMs) have gained significant strength across diverse fields. In this work, we construct an open-source multilingual supervised fine-tuning dataset. The resulting UltraLink dataset comprises approximately 1 million samples across five languages.
  arXiv  Detail & Related papers  (2024-02-07T05:05:53Z)
• GlotLID: Language Identification for Low-Resource Languages [51.38634652914054]
  GlotLID-M is an LID model that satisfies the desiderata of wide coverage, reliability and efficiency. It identifies 1665 languages, a large increase in coverage compared to prior work.
  arXiv  Detail & Related papers  (2023-10-24T23:45:57Z)
• An Open Dataset and Model for Language Identification [84.15194457400253]
  We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033 across 201 languages. We make both the model and the dataset available to the research community.
  arXiv  Detail & Related papers  (2023-05-23T08:43:42Z)
• Multilingual Multimodal Learning with Machine Translated Text [27.7207234512674]
  We investigate whether machine translating English multimodal data can be an effective proxy for the lack of readily available multilingual data. We propose two metrics for automatically removing such translations from the resulting datasets. In experiments on five tasks across 20 languages in the IGLUE benchmark, we show that translated data can provide a useful signal for multilingual multimodal learning.
  arXiv  Detail & Related papers  (2022-10-24T11:41:20Z)
• Bootstrapping Multilingual Semantic Parsers using Large Language Models [28.257114724384806]
  translate-train paradigm of transferring English datasets across multiple languages remains to be the key ingredient for training task-specific multilingual models. We consider the task of multilingual semantic parsing and demonstrate the effectiveness and flexibility offered by large language models (LLMs) for translating English datasets into several languages via few-shot prompting.
  arXiv  Detail & Related papers  (2022-10-13T19:34:14Z)
• X-FACT: A New Benchmark Dataset for Multilingual Fact Checking [21.2633064526968]
  We introduce X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers.
  arXiv  Detail & Related papers  (2021-06-17T05:09:54Z)
• Beyond English-Centric Multilingual Machine Translation [74.21727842163068]
  We create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT.
  arXiv  Detail & Related papers  (2020-10-21T17:01:23Z)
• 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)
• Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation [54.52971020087777]
  Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. Self-supervision improves zero-shot translation quality in multilingual models. We get up to 33 BLEU on ro-en translation without any parallel data or back-translation.
  arXiv  Detail & Related papers  (2020-05-11T00:20:33Z)

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.