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ARXIV:2603.17220 · LANGUAGE PRESERVATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.17220LANGUAGE PRESERVATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity.
Opportunity summary
Pain TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity. The Tharu language, an Indo-Aryan vernacular spoken by approximately 1.7 million people across the…
The rapid proliferation of Large Language Models (LLMs) has created a profound digital divide, effectively excluding indigenous languages of the Global South from the AI revolution. The Tharu language, an Indo-Aryan vernacular spoken by…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through a rigorous empirical ablation study, we demonstrate that despite these imperfections, small-scale synthetic data is highly effective, increasing the dataset volume from 25%…
Language Preservation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity.
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10.48550/arXiv.2603.17220TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity.
Abstract
The rapid proliferation of Large Language Models (LLMs) has created a profound digital divide, effectively excluding indigenous languages of the Global South from the AI revolution. The Tharu language, an Indo-Aryan vernacular spoken by approximately 1.7 million people across the Terai belt of Nepal and India, exemplifies this crisis. Despite a rich oral tradition, Tharu suffers from severe data scarcity and linguistic fragmentation, causing state-of-the-art multilingual models to routinely "hallucinate" or default to dominant high-resource neighbors like Hindi and Nepali due to contamination in pre-training corpora. This paper presents Tharu-LLaMA (3B), a specialized instruction-following model designed to address this exclusion. We introduce TharuChat, a novel dataset constructed via a LLM-to-Human bootstrapping pipeline. We utilized prompt-engineered Gemini models, fed with Rana Tharu grammar and folklore, to synthesize training data. Unlike curated gold-standard corpora, TharuChat reflects the noisy, heterogeneous linguistic reality of the region: it is predominantly anchored in Rana Tharu (~70%) while integrating elements of Dangaura and Kochila dialects. We provide a transparent analysis of the dataset's limitations, including dialectal code-mixing and residual Awadhi/Hindi influence. Through a rigorous empirical ablation study, we demonstrate that despite these imperfections, small-scale synthetic data is highly effective, increasing the dataset volume from 25% to 100% results in a linear reduction in perplexity from 6.42 to 2.88. The resulting model serves as a proof-of-concept for the preservation of under-resourced Himalayan languages via generative AI, achievable on consumer-grade hardware.
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Dimensions overall score 7.0
PROBLEM
TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity. The Tharu language, an Indo-Aryan vernacular spoken by approximately 1.7 million people across the Terai belt of Nepal an...
METHOD
The rapid proliferation of Large Language Models (LLMs) has created a profound digital divide, effectively excluding indigenous languages of the Global South from the AI revolution. The Tharu language, an Indo-Aryan vernacular spoken by approximately 1.7 million people across th...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through a rigorous empirical ablation study, we demonstrate that despite these imperfections, small-scale synthetic data is highly effective, increasing the dataset volume from 25% to 100% results in a li...
WHY NOW
Language Preservation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity. The Tharu language, an Indo-Aryan vernacular spoken by approximately 1.7 million people across the Terai belt of Nepal and India, exemplifies this crisis.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The rapid proliferation of Large Language Models (LLMs) has created a profound digital divide, effectively excluding indigenous languages of the Global South from the AI revolution. The Tharu language, an Indo-Aryan vernacular spoken by approximately 1.7 million people across the Terai belt of Nepal and India, exemplifies this crisis.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through a rigorous empirical ablation study, we demonstrate that despite these imperfections, small-scale synthetic data is highly effective, increasing the dataset volume from 25% to 100% results in a linear reduction in perplexity from 6.42 to 2.88.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Language Preservation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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TharuChat leverages synthetic data and human validation to bootstrap a language model for the under-resourced Tharu language, promoting linguistic diversity.
Segment
Language Preservation
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