  In this paper, we explore the challenges associated with establishing an
end-to-end fact-checking pipeline in a real-world context, covering over 90
languages. Our real-world experimental benchmarks demonstrate that fine-tuning
Transformer models specifically for fact-checking tasks, such as claim
detection and veracity prediction, provide superior performance over large
language models (LLMs) like GPT-4, GPT-3.5-Turbo, and Mistral-7b. However, we
illustrate that LLMs excel in generative tasks such as question decomposition
for evidence retrieval. Through extensive evaluation, we show the efficacy of
fine-tuned models for fact-checking in a multilingual setting and complex
claims that include numerical quantities.
