Paper Revision 2024.lrec-main.561, closes #4310.

data/xml/2024.lrec.xml

@@ -6643,8 +6643,10 @@
       <author><first>Seid Muhie</first><last>Yimam</last></author>
       <pages>6341–6352</pages>
       <abstract>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.</abstract>
-      <url hash="453734d9">2024.lrec-main.561</url>
+      <url hash="02c49760">2024.lrec-main.561</url>
       <bibkey>tonja-etal-2024-ethiollm</bibkey>
+      <revision id="1" href="2024.lrec-main.561v1" hash="453734d9"/>
+      <revision id="2" href="2024.lrec-main.561v2" hash="02c49760" date="2025-01-12">Updates the affiliation of two of the authors.</revision>
     </paper>
     <paper id="562">
       <title><fixed-case>E</fixed-case>uropean Language Grid: One Year after</title>