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EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation

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arxiv 2403.13737 v4 pith:GVPGGM34 submitted 2024-03-20 cs.CL

Atnafu Lambebo Tonja , Israel Abebe Azime , Tadesse Destaw Belay , Mesay Gemeda Yigezu , Moges Ahmed Mehamed , Abinew Ali Ayele , Ebrahim Chekol Jibril , Michael Melese Woldeyohannis
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Olga Kolesnikova Philipp Slusallek Dietrich Klakow Shengwu Xiong Seid Muhie Yimam
This is my paper · ORCID
classification cs.CL
keywords modelslanguagedownstreamlanguagestasksethiopianlargemultilingual
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Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. Ethiopian languages exhibit remarkable linguistic diversity, encompassing a wide array of scripts, and are imbued with profound religious and cultural significance. This paper introduces EthioLLM -- multilingual large language models for five Ethiopian languages (Amharic, Ge'ez, Afan Oromo, Somali, and Tigrinya) and English, and Ethiobenchmark -- a new benchmark dataset for various downstream NLP tasks. We evaluate the performance of these models across five downstream NLP tasks. We open-source our multilingual language models, new benchmark datasets for various downstream tasks, and task-specific fine-tuned language models and discuss the performance of the models. Our dataset and models are available at the https://huggingface.co/EthioNLP repository.

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Cited by 4 Pith papers

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  1. Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding

    cs.CL 2024-12 conditional novelty 7.0 of 10

    A new multi-label emotion benchmark for four Ethiopian languages shows that fine-tuned encoder-only models outperform zero-shot and few-shot large language models, with large gaps between resource-rich and resource-po...

  2. Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A new 50-conversation benchmark shows commercial ASR and diarization models degrade notably on African-accented conversational English.

  3. Amplify Initiative: Building A Localized Data Platform for Globalized AI

    cs.HC 2025-04 conditional novelty 5.0 of 10

    Amplify's pilot with 155 experts produced an annotated dataset of 8,091 adversarial queries in seven languages, intended to evaluate AI safety and cultural relevance in Sub-Saharan Africa.

  4. The State of Large Language Models for African Languages: Progress and Challenges

    cs.AI 2025-06 conditional novelty 4.0 of 10

    Across 20 reviewed language models, only about 42 of Africa's 2,000+ languages receive any support, and just 3 of 23 active scripts are commonly used.

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