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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.22720 |
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| _version_ | 1866917091312926720 |
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| author | Das, Amit Hasan, Md. Najib Sarkar, Souvika Zhang, Zheng Jamshidi, Fatemeh Bhattacharya, Tathagata Raychawdhury, Nilanjana Feng, Dongji Jain, Vinija Chadha, Aman |
| author_facet | Das, Amit Hasan, Md. Najib Sarkar, Souvika Zhang, Zheng Jamshidi, Fatemeh Bhattacharya, Tathagata Raychawdhury, Nilanjana Feng, Dongji Jain, Vinija Chadha, Aman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22720 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating Hallucination in Conversations for Low Resource Languages Das, Amit Hasan, Md. Najib Sarkar, Souvika Zhang, Zheng Jamshidi, Fatemeh Bhattacharya, Tathagata Raychawdhury, Nilanjana Feng, Dongji Jain, Vinija Chadha, Aman Computation and Language 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. |
| title | Investigating Hallucination in Conversations for Low Resource Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.22720 |