IndicTalk: A Large-Scale Persona-Based Multilingual Conversational Corpus for Indic Languages

Abstract:Large Language Models (LLMs) have transformed conversational AI, yet high-quality multilingual code-mixed dialogue resources remain scarce, particularly for Indic languages where speakers naturally alternate between English and their native language in both native-script and Romanized forms. We present IndicTalk, one of the largest multilingual Indic code-mixed conversational corpora, comprising over 13,28,604 event-grounded multi-turn conversations across 18 language varieties covering 9 Indic languages. The corpus is generated through a fully automated pipeline that combines real-world news grounding, persona-conditioned dialogue generation using multilingual LLMs, and automatic quality validation. Extensive linguistic, automatic, and human evaluations demonstrate that IndicTalk produces fluent, coherent, and naturally code-mixed conversations across both script variants. We will release IndicTalk to support the development and evaluation of multilingual conversational AI for underrepresented Indic languages. The dataset is available at: this https URL .

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2607.23242 [cs.CL]

(or arXiv:2607.23242v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.23242 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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