# Large Language Models for Low-Resource Languages: A Conceptual Framework for an Electronic Explanatory Dictionary of the Tajik Language

arXiv: https://arxiv.org/abs/2608.04186 · HTML: https://mlsanity.com/paper/2608.04186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 71 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [SeaLLMs -- Large Language Models for Southeast Asia](https://mlsanity.com/paper/2312.00738/) (2023)
- [Cheetah: Natural Language Generation for 517 African Languages](https://mlsanity.com/paper/2401.01053/) (2024)
- [AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages](https://mlsanity.com/paper/2601.06395/) (2026)
- [How Good are Commercial Large Language Models on African Languages?](https://mlsanity.com/paper/2305.06530/) (2023)
- [Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models](https://mlsanity.com/paper/2411.01141/) (2024)
- [Generating bilingual example sentences with large language models as lexicography assistants](https://mlsanity.com/paper/2410.03182/) (2024)
- [A Systematic Benchmark of Machine Transliteration Models for the Tajik-Farsi Language Pair: A Comparative Study from Rule-Based to Transformer Architectures](https://mlsanity.com/paper/2605.02270/) (2026)
- [LexGen: Domain-aware Multilingual Lexicon Generation](https://mlsanity.com/paper/2405.11200/) (2024)
- [TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP](https://mlsanity.com/paper/2605.06886/) (2026)
- [Character-Level Transformer for Tajik-Persian Transliteration with a Parallel Lexical Corpus](https://mlsanity.com/paper/2605.09092/) (2026)
- [Soro: A Lightweight Foundation Model and Chatbot for Tajik](https://mlsanity.com/paper/2605.27379/) (2026)
- [Multilingual Definition Modeling](https://mlsanity.com/paper/2506.01489/) (2025)
- [Benchmarking Parameter-Efficient Fine-Tuning of Large Language Models for Low-Resource Tajik Text Generation with the Tajik Web Corpus](https://mlsanity.com/paper/2605.03742/) (2026)
- [Benchmarking POS Tagging for the Tajik Language: A Comparative Study of Neural Architectures on the TajPersParallel Corpus](https://mlsanity.com/paper/2605.04576/) (2026)
- [TajikNLP: An Open-Source Toolkit for Comprehensive Text Processing of Tajik (Cyrillic Script)](https://mlsanity.com/paper/2605.04583/) (2026)

## Abstract (arXiv)

This paper presents a conceptual framework for developing an electronic explanatory dictionary of the Tajik language using large language models (LLMs). The relevance of the work stems from the absence of a comprehensive digital lexicographic resource for Tajik that is comparable in functionality to dictionaries for high-resource languages, and from the limited adaptation of modern natural language processing technologies to low-resource language systems. Based on a systematic survey of existing linguistic, statistical, and corpus resources, we propose a dictionary architecture that integrates modules for morphological analysis, lemmatization, semantic clustering, and dictionary entry generation using LLMs. The choice of subword tokenization is justified by the agglutinative nature of Tajik morphology and its high morphological variability, along with a parameter-efficient fine-tuning (PEFT) strategy suitable for limited annotated data. The novelty of the work lies in proposing the first holistic conceptual architecture of an explanatory dictionary for Tajik that unifies classical lexicographic methods, language statistics, and generative capabilities of LLMs into a single system. The practical significance of the study is the formation of a methodological foundation for developing a full-featured electronic dictionary that can serve both as a lexicographic tool and as a core resource for machine translation, automatic summarization, sentiment analysis, and other applied NLP tasks. The paper is intended for specialists in computational linguistics, lexicography, and developers of natural language processing systems working with low-resource languages.