Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood O. Yekini, Jonas Kemp, Katherine A Heller, Jude Chidubem Omeke, Chidi Asuzu Md, Naome A Etori, Aïmérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael L. Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Mardhiyah Sanni, Tassallah Abdullahi, Devendra Deepak Kayande, Emmanuel Ayodele, Naome A Etori, Michael Samwel Mollel, Moshood O. Yekini, Chibuzor Okocha, Lukman Enegi Ismaila, Folafunmi Omofoye, Boluwatife A. Adewale, Tobi Olatunji. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Models that generate natural language explanations (NLEs) for their predictions have recently gained increasing interest.However, this approach usually demands large datasets of human-written NLEs for the ground-truth answers at training time, which can be expensive and potentially infeasible for some applications.When only a few NLEs are available (a fewshot setup), fine-tuning pre-trained language models (PLMs) in conjunction with promptbased learning has recently shown promising results.However, PLMs typically have billions of parameters, making full fine-tuning expensive.We propose SPARSEFIT, a sparse few-shot finetuning strategy that leverages discrete prompts to jointly generate predictions and NLEs.We experiment with SPARSEFIT on three sizes of the T5 language model and four datasets and compare it against existing state-of-the-art Parameter-Efficient Fine-Tuning (PEFT) techniques.We find that fine-tuning only 6.8% of the model parameters leads to competitive results for both the task performance and the quality of the generated NLEs compared to full finetuning of the model and produces better results on average than other PEFT methods in terms of predictive accuracy and NLE quality.