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From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages

arXiv 2606.26112 ↗First posted 21 May 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Low-resource languages face a critical challenge in AI development: creating specialized conversational systems without access to massive training corpora. We present a systematic methodology for transforming structured linguistic resources into specialized AI systems, demonstrating that expert-curated lexical databases can serve as effective foundations for conversational AI development. Our approach converts Hindi WordNet into 1.25 million diverse instruction-response pairs, fine-tunes a 12B-parameter language model using resource-efficient LoRA with 4-bit quantization. Evaluation through a Hindi language learning chatbot demonstrates that structured-knowledge-based systems achieve superior pedagogical effectiveness (91.0 vs. 79.4-83.6 for general-purpose models) while maintaining competitive semantic performance and exceptional consistency. The complete pipeline demonstrates a proof-of-concept methodology using Hindi for developing specialized AI systems for any languages with WordNet resources. This work addresses the critical gap in AI accessibility for low-resource languages, offering a practical alternative to corpus-intensive approaches and potentially enabling specialized AI development for the hundreds of languages with existing WordNet resources.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 36 references that are in the map. First posted on arXiv 21 May 2026.

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References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  3. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
  4. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 593
  5. 2023Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Promptscited by 45
  6. 2024Foundation Models for Low-Resource Language Education (Vision Paper)cited by 5

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  1. 2024Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus
  2. 2026Natural Language Processing: A Comprehensive Practical Guide from Tokenisation to RLHF
  3. 2025Llama-3-Nanda-10B-Chat: An Open Generative Large Language Model for Hindi
  4. 2026TAMUSA-Chat: A Domain-Adapted Large Language Model Conversational System for Research and Responsible Deployment
  5. 2024SUTRA: Scalable Multilingual Language Model Architecture
  6. 2024Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs
  7. 2024CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks
  8. 2025Small Language Models are the Future of Agentic AI