mlsanityData to 7 Oct 2026

Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish

arXiv 2403.00411 ↗First posted 1 Mar 2024cs.CLcited by 4

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Abstract (arXiv)

The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language. Hence, there is a scarcity of datasets for other languages, including Turkish. To address this concern, we have introduced the FCTR dataset, consisting of 3238 real-world claims. This dataset spans multiple domains and incorporates evidence collected from three Turkish fact-checking organizations. Additionally, we aim to assess the effectiveness of cross-lingual transfer learning for low-resource languages, with a particular focus on Turkish. We demonstrate in-context learning (zero-shot and few-shot) performance of large language models in this context. The experimental results indicate that the dataset has the potential to advance research in the Turkish language.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 53 of its 102 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

53 of 102 references.

References · 53

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Researchcited by 26
  2. 2024Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategiescited by 10
  3. 2025When Scale Meets Diversity: Evaluating Language Models on Fine-Grained Multilingual Claim Verificationcited by 3
  4. 2026SEEK: Semantic Evidence Extraction via Adaptive ChunKing for Multilingual Fact-Checkingcited by 0