Investigating Hallucination in Conversations for Low Resource Languages
Authors: name: Amit Das, name: Md. Najib Hasan, name: Souvika Sarkar, name: Zheng Zhang, name: Fatemeh Jamshidi, name: Tathagata Bhattacharya, name: Nilanjana Raychawdhury, name: Dongji Feng, name: Vinija Jain, name: Aman Chadha

Large Language Models (LLMs) have demonstrated remarkable proficiency in
generating text that closely resemble human writing. However, they often
generate factually incorrect statements, a problem typically referred to as
'hallucination'. Addressing hallucination is crucial for enhancing the
reliability and effectiveness of LLMs. While much research has focused on
hallucinations in English, our study extends this investigation to
conversational data in three languages: Hindi, Farsi, and Mandarin. We offer a
comprehensive analysis of a dataset to examine both factual and linguistic
errors in these languages for GPT-3.5, GPT-4o, Llama-3.1, Gemma-2.0,
DeepSeek-R1 and Qwen-3. We found that LLMs produce very few hallucinated
responses in Mandarin but generate a significantly higher number of
hallucinations in Hindi and Farsi.