A Pāninian Foundation for Indic Language Processing

• URL: http://arxiv.org/abs/2606.24172v1
• Date: Tue, 23 Jun 2026 05:53:37 GMT
• Title: A Pāninian Foundation for Indic Language Processing
• Abstract summary: Indic languages came to share a morphosyntactic architecture formalized in Pnini's grammar, the Astdhyy.<n>We argue that this Pninian framework supplies a unifying computational architecture the field has lacked.<n>We propose a four-part benchmark suite to render this shared architecture explicit, measurable, and ready to be leveraged.
• Score: 16.164759165132935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: More than a billion people communicate in Indic languages, yet the natural language processing infrastructure serving them remains fragmented and underdeveloped. The cause is structural: the field organizes its tools and benchmarks around individual languages or small subsets of genealogical language families, building separate analyzers, parsers, and datasets for each language and starting over for the next. This overlooks a deep regularity. Through more than two millennia of convergence around Sanskrit, Indic languages came to share a morphosyntactic architecture formalized in Pānini's grammar, the Astādhyāyī. This cuts across genealogical lines, uniting languages through a common framework. We argue that this Pāninian framework supplies a unifying computational architecture the field has lacked, and that benchmarks grounded explicitly in it would make Indic language systems more accurate, more data-efficient, and more transferable, effectively merging many apparently disparate and sparse Indic language resources into a single high-resource metalanguage bedrock. We propose a four-part benchmark suite to render this shared architecture explicit, measurable, and ready to be leveraged for practical applications. Moreover, we underscore the question it raises for interpretability research: whether neural models trained on these languages come to represent Pānini's categories on their own.

Related papers

• MameLoshnLM: Yiddish Language Model and Evaluation Benchmark [62.936233688546984]
  We present MameLoshnLM, the first open-source 8B- parameter language model built specifically for Yiddish.<n>Despite Yiddish's rich textual tradition, its limited digital presence and the scarcity of reliable evaluation resources have constrained progress in Yiddish language modeling.<n>Our results provide both a foundation for Yiddish NLP and a practical template for language model development in historically rich but digitally underrepresented languages.
  arXiv  Detail & Related papers  (2026-08-06T10:24:08Z)
• Multilinguality of Large Language Models From a Structural Perspective [55.097821802807566]
  Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data.<n>Our findings reveal that low-resource languages are structurally more different from English than high- and mid-resource languages.
  arXiv  Detail & Related papers  (2026-06-01T07:18:09Z)
• CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems [18.521673953685575]
  India's linguistic landscape is one of the most diverse in the world, comprising over 120 major languages and approximately 1,600 additional languages.<n>Despite recent progress in multilingual neural machine translation (NMT), high-quality parallel corpora for Indian languages remain scarce.<n>In this paper, we introduce a large-scale, high-quality annotated parallel corpus covering 11 languages.
  arXiv  Detail & Related papers  (2025-09-24T09:48:26Z)
• PARAM-1 BharatGen 2.9B Model [14.552007884700618]
  PARAM-1 is a 2.9B parameter decoder-only, text-only language model trained from scratch with an explicit architectural and linguistic focus on Indian diversity.<n>It is guided by three core principles: equitable representation of Indic languages through a 25% corpus allocation; tokenization fairness via a SentencePiece tokenizer adapted to Indian morphological structures; and culturally aligned evaluation benchmarks across IndicQA, code-mixed reasoning, and socio-linguistic robustness tasks.
  arXiv  Detail & Related papers  (2025-07-16T06:14:33Z)
• IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding [2.062076715606512]
  Known by more than 1.5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research.<n>IndicMMLU-Pro is a benchmark designed to evaluate Large Language Models (LLMs) across Indic languages.
  arXiv  Detail & Related papers  (2025-01-27T03:19:03Z)
• Navigating Text-to-Image Generative Bias across Indic Languages [53.92640848303192]
  This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their performance in English.
  arXiv  Detail & Related papers  (2024-08-01T04:56:13Z)
• No Language Left Behind: Scaling Human-Centered Machine Translation [69.28110770760506]
  We create datasets and models aimed at narrowing the performance gap between low and high-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-11T07:33:36Z)
• Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages [34.79533646549939]
  We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. Low resource languages, such as Oriya and Punjabi, are found to be the largest beneficiaries of multilingual fine-tuning.
  arXiv  Detail & Related papers  (2021-09-22T06:37:39Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• HinFlair: pre-trained contextual string embeddings for pos tagging and text classification in the Hindi language [0.0]
  HinFlair is a language representation model (contextual string embeddings) pre-trained on a large monolingual Hindi corpus. Results show that HinFlair outperforms previous state-of-the-art publicly available pre-trained embeddings for downstream tasks like text classification and pos tagging.
  arXiv  Detail & Related papers  (2021-01-18T09:23:35Z)
• SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection [81.85463892070085]
  The SIGMORPHON 2020 task on morphological reinflection aims to investigate systems' ability to generalize across typologically distinct languages. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages.
  arXiv  Detail & Related papers  (2020-06-20T13:24:14Z)
• Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations [83.27475281544868]
  We use singular vector canonical correlation analysis to study what kind of information is induced from each source. We observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
  arXiv  Detail & Related papers  (2020-04-30T16:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.