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

2026-07-25 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

IndicTalk is a newly introduced large-scale persona-based multilingual conversational corpus designed to address the scarcity of high-quality code-mixed dialogue resources for Indic languages. This corpus comprises over 13,28,604 event-grounded multi-turn conversations, spanning 18 language varieties across 9 Indic languages, including both native-script and Romanized forms. It was generated using a fully automated pipeline that integrates real-world news grounding, persona-conditioned dialogue generation via multilingual LLMs, and automated quality validation. Extensive linguistic, automatic, and human evaluations confirm that IndicTalk produces fluent, coherent, and naturally code-mixed dialogues. The dataset will be publicly released to foster the development and evaluation of multilingual conversational AI for underrepresented Indic languages.

Key takeaway

For NLP Engineers developing conversational AI for South Asian markets, IndicTalk offers a critical resource to overcome data scarcity. You should integrate this 1.3M+ conversation corpus to train and fine-tune models, ensuring they handle natural code-mixing and diverse script variants in Indic languages. This directly improves model fluency and coherence for real-world user interactions, accelerating deployment in underrepresented language communities.

Key insights

IndicTalk provides a large, high-quality, persona-based multilingual conversational corpus for underrepresented Indic languages.

Principles

• Code-mixing is natural in Indic language conversations.
• Automated pipelines can generate large, high-quality dialogue corpora.
• Persona conditioning enhances dialogue realism.

Method

The corpus generation pipeline combines real-world news grounding, persona-conditioned dialogue generation using multilingual LLMs, and automatic quality validation to create fluent, code-mixed conversations.

In practice

• Utilize IndicTalk for training multilingual conversational AI.
• Evaluate LLMs on code-mixed Indic language dialogues.
• Explore automated corpus generation for other low-resource languages.

Topics

• IndicTalk Corpus
• Multilingual Conversational AI
• Code-mixing
• Indic Languages
• Dialogue Generation
• Large Language Models

Best for: Research Scientist, NLP Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.