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20242026
most citedBridging Gaps in Natural Language Processing for Yorùbá: A Systematic Review of a Decade of Progress and Prospects

5 citations · 5 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CL2026

SemiAdapt-Instruct: Extensible Instruction Tuning via Latent Domain-Specialised Adapters

Josh McGiff, Salma Mekaoui, Robert Shanahan +1

Instruction-tuned LLMs are deployed into environments where domains evolve, yet extending a fine-tuned model's capabilities without full retraining remains an unsolved practical ch…

cs.CL20265 cited

Bridging Gaps in Natural Language Processing for Yorùbá: A Systematic Review of a Decade of Progress and Prospects

Toheeb Aduramomi Jimoh, Tabea De Wille, Nikola S. Nikolov

Natural Language Processing (NLP) is becoming a dominant subset of artificial intelligence as the need to help machines understand human language looks indispensable. Several NLP a…

cs.CL2025

Irish-BLiMP: A Linguistic Benchmark for Evaluating Human and Language Model Performance in a Low-Resource Setting

Josh McGiff, Khanh-Tung Tran, William Mulcahy +7

We present Irish-BLiMP (Irish Benchmark of Linguistic Minimal Pairs), the first dataset and framework designed for fine-grained evaluation of linguistic competence in the Irish lan…

cs.CL2025

SemiAdapt and SemiLoRA: Efficient Domain Adaptation for Transformer-based Low-Resource Language Translation with a Case Study on Irish

Josh McGiff, Nikola S. Nikolov

Fine-tuning is widely used to tailor large language models for specific tasks such as neural machine translation (NMT). However, leveraging transfer learning is computationally exp…

cs.CL2025

Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review

Josh McGiff, Nikola S. Nikolov

Generative language modelling has surged in popularity with the emergence of services such as ChatGPT and Google Gemini. While these models have demonstrated transformative potenti…

cs.CL2024

Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter

Josh McGiff, Nikola S. Nikolov

Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research. Utilising advanced…