2026-10-17T17:57:50Z https://lifeofresearch.com/oai2

oai:lifeofresearch.com:13 2026-09-22T17:33:56Z type:article openaire

10.5281/zenodo.19854165 https://lifeofresearch.com/articles/13 Pranay Mahendrakar 0009-0003-7224-029X Life of Research 2026 Journal article Creative Commons Attribution 4.0 Multilingual hallucination in large language models has begun to receive systematic attention. Recent work has measured aggregate hallucination rates across 19 to 30 languages (FACTSCORE-multilingual, MFAVA, Poly-FEVER), introduced multilingual benchmarks (HalluVerseM3, BHRAM-IL for some Indo-Aryan languages), and demonstrated that LLMs hallucinate more in low-resource languages than in English. What this growing literature does not yet provide is a typology-aware comparison of how hallucination patterns — not just rates — differ across closely-related languages. The Dravidian languages (Tamil, Telugu, Kannada, Malayalam) and the Indo-Aryan languages (Hindi, Marathi, Bengali, Gujarati) of India offer a natural experimental setting: substantial variation in training data quantity, sharply different morphological typology (Dravidian agglutination versus Indo-Aryan fusion, presence or absence of grammatical gender), shared script families in some cases, and pervasive code-mixing with English in real use. This paper makes three claims. First, the question "how often does the model hallucinate in language X" is the wrong primary question; the more informative question is "which categories of hallucination dominate in language X, and why." Second, four hypotheses about Indic-language hallucination patterns can be distinguished empirically and have meaningful policy and research implications. Third, the experimental work needed to test these hypotheses is small enough that a single research team in India could execute it within a year. We propose a measurement framework, a concrete experimental protocol, and a case for why this question is particularly tractable in the Indian research context.