Turk-LettuceDetect: A Hallucination Detection Models for Turkish RAG
  Applications
Authors: name: Selva Taş, name: Mahmut El Huseyni, name: Özay Ezerceli, name: Reyhan Bayraktar, name: Fatma Betül Terzioğlu

The widespread adoption of Large Language Models (LLMs) has been hindered by
their tendency to hallucinate, generating plausible but factually incorrect
information. While Retrieval-Augmented Generation (RAG) systems attempt to
address this issue by grounding responses in external knowledge, hallucination
remains a persistent challenge, particularly for morphologically complex,
low-resource languages like Turkish. This paper introduces Turk-LettuceDetect,
the first suite of hallucination detection models specifically designed for
Turkish RAG applications. Building on the LettuceDetect framework, we formulate
hallucination detection as a token-level classification task and fine-tune
three distinct encoder architectures: a Turkish-specific ModernBERT,
TurkEmbed4STS, and multilingual EuroBERT. These models were trained on a
machine-translated version of the RAGTruth benchmark dataset containing 17,790
instances across question answering, data-to-text generation, and summarization
tasks. Our experimental results show that the ModernBERT-based model achieves
an F1-score of 0.7266 on the complete test set, with particularly strong
performance on structured tasks. The models maintain computational efficiency
while supporting long contexts up to 8,192 tokens, making them suitable for
real-time deployment. Comparative analysis reveals that while state-of-the-art
LLMs demonstrate high recall, they suffer from low precision due to
over-generation of hallucinated content, underscoring the necessity of
specialized detection mechanisms. By releasing our models and translated
dataset, this work addresses a critical gap in multilingual NLP and establishes
a foundation for developing more reliable and trustworthy AI applications for
Turkish and other languages.