Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs
Researchers have developed a multilingual fact-checking system for Factiverse, utilizing fine-tuned compact models for efficiency and scalability. The system employs a three-stage pipeline involving claim detection, evidence retrieval, and veracity prediction. Comparative experiments showed that fine-tuned models like XLM-RoBERTa-Large and mmBERT-base offer strong performance across numerous languages, remaining competitive with larger LLMs such as GPT-5.2 and Claude Opus 4.6 in terms of accuracy and significantly outperforming them in latency and cost-efficiency for production deployments. AI
IMPACT Demonstrates the viability of smaller, fine-tuned models for efficient, large-scale multilingual AI applications.