Investigating Hallucination in Conversations for Low Resource Languages

• URL: http://arxiv.org/abs/2507.22720v1
• Date: Wed, 30 Jul 2025 14:39:51 GMT
• Title: Investigating Hallucination in Conversations for Low Resource Languages
• Authors: Amit Das, Md. Najib Hasan, Souvika Sarkar, Zheng Zhang, Fatemeh Jamshidi, Tathagata Bhattacharya, Nilanjana Raychawdhury, Dongji Feng, Vinija Jain, Aman Chadha,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text that closely resembles human writing.<n>They often generate factually incorrect statements, a problem typically referred to as 'hallucination'<n>This study extends this investigation to conversational data in three languages: Hindi, Farsi, and Mandarin.<n>We found that LLMs produce very few hallucinated responses in Mandarin but generate a significantly higher number of hallucinations in Hindi and Farsi.
• Score: 6.439114994667614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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