17 citations · 18 across the 7 of their papers we have counts for
7 papers
Expanding the Lexicon of Ge'ez Based African Languages: A Comparative Study of Amharic and Tigrinya
Hailay Kidu Teklehaymanot, Debela Desalegn Yadeta, Wolfgang Nejdl
Multilingual pre-trained language models such as XLM-R perform well for major languages but struggle with low-resource Ge'ez-script languages, largely because Latin-script-centric…
A Sovereign, Open-Source Foundation Model for German and English
Soofi-Team, :, Benedikt Droste +30
We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English. Its hybrid design activates only 3B…
Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
Hailay Kidu Teklehaymanot, Wolfgang Nejdl
Tokenization disparities pose a significant barrier to achieving equitable access to artificial intelligence across linguistically diverse populations. This study conducts a large-…
Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks
Hailay Kidu Teklehaymanot, Gebrearegawi Gidey, Wolfgang Nejdl
Despite advances in Neural Machine Translation (NMT), low-resource languages like Tigrinya remain underserved due to persistent challenges, including limited corpora, inadequate to…
MoVoC: Morphology-Aware Subword Construction for Geez Script Languages Corrected version
Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl
Subword-based tokenization methods often fail to preserve morphological boundaries, a limitation especially pronounced in low-resource, morphologically complex languages such as th…
TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya
Hailay Teklehaymanot, Dren Fazlija, Niloy Ganguly +2
The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources.This study…
Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions
Soumyadeep Roy, Aparup Khatua, Fatemeh Ghoochani +3
GPT-4 demonstrates high accuracy in medical QA tasks, leading with an accuracy of 86.70%, followed by Med-PaLM 2 at 86.50%. However, around 14% of errors remain. Additionally, curr…