BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languages
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arXiv
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| Format: | Preprint |
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2025
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| author | Terdalkar, Hrishikesh Bhojani, Kirtan Dongare, Aryan Behera, Omm Aditya |
| author_facet | Terdalkar, Hrishikesh Bhojani, Kirtan Dongare, Aryan Behera, Omm Aditya |
| contents | Large language models (LLMs) are increasingly deployed in multilingual applications but often generate plausible yet incorrect or misleading outputs, known as hallucinations. While hallucination detection has been studied extensively in English, under-resourced Indian languages remain largely unexplored. We present BHRAM-IL, a benchmark for hallucination recognition and assessment in multiple Indian languages, covering Hindi, Gujarati, Marathi, Odia, along with English. The benchmark comprises 36,047 curated questions across nine categories spanning factual, numerical, reasoning, and linguistic tasks. We evaluate 14 state-of-the-art multilingual LLMs on a benchmark subset of 10,265 questions, analyzing cross-lingual and factual hallucinations across languages, models, scales, categories, and domains using category-specific metrics normalized to (0,1) range. Aggregation over all categories and models yields a primary score of 0.23 and a language-corrected fuzzy score of 0.385, demonstrating the usefulness of BHRAM-IL for hallucination-focused evaluation. The dataset, and the code for generation and evaluation are available on GitHub (https://github.com/sambhashana/BHRAM-IL/) and HuggingFace (https://huggingface.co/datasets/sambhashana/BHRAM-IL/) to support future research in multilingual hallucination detection and mitigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languages Terdalkar, Hrishikesh Bhojani, Kirtan Dongare, Aryan Behera, Omm Aditya Computation and Language Artificial Intelligence Emerging Technologies I.2; I.7 Large language models (LLMs) are increasingly deployed in multilingual applications but often generate plausible yet incorrect or misleading outputs, known as hallucinations. While hallucination detection has been studied extensively in English, under-resourced Indian languages remain largely unexplored. We present BHRAM-IL, a benchmark for hallucination recognition and assessment in multiple Indian languages, covering Hindi, Gujarati, Marathi, Odia, along with English. The benchmark comprises 36,047 curated questions across nine categories spanning factual, numerical, reasoning, and linguistic tasks. We evaluate 14 state-of-the-art multilingual LLMs on a benchmark subset of 10,265 questions, analyzing cross-lingual and factual hallucinations across languages, models, scales, categories, and domains using category-specific metrics normalized to (0,1) range. Aggregation over all categories and models yields a primary score of 0.23 and a language-corrected fuzzy score of 0.385, demonstrating the usefulness of BHRAM-IL for hallucination-focused evaluation. The dataset, and the code for generation and evaluation are available on GitHub (https://github.com/sambhashana/BHRAM-IL/) and HuggingFace (https://huggingface.co/datasets/sambhashana/BHRAM-IL/) to support future research in multilingual hallucination detection and mitigation. |
| title | BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languages |
| topic | Computation and Language Artificial Intelligence Emerging Technologies I.2; I.7 |
| url | https://arxiv.org/abs/2512.01852 |