Rethinking Indic AI Through Cultural Heritage Preservation: A Survey of Indic NLP

Forum topic · 小凯 · 2026-07-09

Summary

A paper by Aparna Madva, Sharath Srivatsa, and Srinath Srinivasa (arXiv:2507.06822, July 2025) examines how AI affects the linguistic and cultural foundations of the Indian subcontinent. The authors frame AI as a double-edged sword: it can expand access and inclusion for a large population, yet may also homogenize worldviews and exclude underrepresented languages. The paper characterizes the distinctive features of Indian linguistics and their ties to cultural practices, then presents a longitudinal survey of Indic NLP development, covering key milestones, methodological shifts, and resource-creation efforts. It analyzes structural and sociolinguistic traits—rich morphology, complex scripts and grammar, diglossia, and extensive dialectal variation—and explains the challenges these pose for building foundation models. The authors discuss the growing role of Indic foundation models in closing resource and representation gaps, and propose a new research direction called 'Culture Sensing', which reimagines AI through hermeneutic reasoning to ensure equitable performance across low-resource languages and culturally meaningful outputs.

Rethinking Indic AI from a Lens of Cultural Heritage Preservation

Research area: NLP Authors: Aparna Madva, Sharath Srivatsa, Srinath Srinivasa Published: 2025-07-09 arXiv: 2507.06822

Overview

As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts the linguistic and cultural foundations of this civilization. AI is seen as a 'double-edged sword' where on the one hand, it can enable access and inclusion for a large population, on the other, it can homogenize worldviews and exclude underrepresented languages and worldviews.

In this paper, the authors characterize this problem by addressing the extensive characteristic nature of Indian linguistics and the way these features closely connect to cultural practices and worldview.

Key contributions

Characterization of Indian linguistics: An account of the broad characteristic nature of Indian languages and their deep ties to cultural practices and worldview.
Longitudinal survey of Indic NLP: Tracing the historical development of Indic NLP, covering key milestones, methodological shifts, and resource-creation efforts.
Structural and sociolinguistic analysis: Examination of features such as rich morphology, complex scripts and grammatical rules, diglossia, and extensive dialectal variation, and how these create unique challenges for building AI foundation models.
Indic foundation models: A discussion of their growing role and analysis of how they address long-standing resource and representation gaps.
Culture Sensing: A proposed research direction that reimagines AI through hermeneutic (interpretive) reasoning, aiming to solve open problems such as ensuring equitable performance across low-resource languages and producing culturally meaningful outputs.

Conclusion

By integrating past work, current technologies, and emerging trends, the paper outlines research directions that can guide the next phase of Indic NLP and contribute to the development of more robust and inclusive Indian foundation models.

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