arxiv:2412.20223

AfriHG: News headline generation for African Languages

Published on Dec 28, 2024

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Abstract

AfriHG is a 16-language African news headline dataset where a 313M-parameter seq2seq model matches a 13B-parameter LLM, outperforming mT5-base.

Generated by thinkingmachines/Inkling-Small

This paper introduces AfriHG -- a news headline generation dataset created by combining from XLSum and MasakhaNEWS datasets focusing on 16 languages widely spoken by Africa. We experimented with two seq2eq models (mT5-base and AfriTeVa V2), and Aya-101 LLM. Our results show that Africa-centric seq2seq models such as AfriTeVa V2 outperform the massively multilingual mT5-base model. Finally, we show that the performance of fine-tuning AfriTeVa V2 with 313M parameters is competitive to prompting Aya-101 LLM with more than 13B parameters.

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