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20242026
most citedEvaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding

2 citations · 3 across the 9 of their papers we have counts for

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cs.CL2026

AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languages

Israel Abebe Azime, Jesujoba Oluwadara Alabi, Crystina Zhang +16

Assessing the veracity of a claim made online is a complex and important task with real-world implications. When these claims are directed at communities with limited access to inf…

cs.CL2026

AmharicStoryQA: A Multicultural Story Question Answering Benchmark in Amharic

Israel Abebe Azime, Abenezer Kebede Angamo, Hana Mekonen Tamiru +4

With the growing emphasis on multilingual and cultural evaluation benchmarks for large language models, language and culture are often treated as synonymous, and performance is com…

cs.CL2025

Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages

Israel Abebe Azime, Tadesse Destaw Belay, Dietrich Klakow +2

Large language models (LLMs) have demonstrated significant capabilities in solving mathematical problems expressed in natural language. However, multilingual and culturally-grounde…

cs.CL2025★ 2 cited

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

Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele +5

Large Language Models (LLMs) show promising learning and reasoning abilities. Compared to other NLP tasks, multilingual and multi-label emotion evaluation tasks are under-explored…

cs.CL2024

ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding

Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay +11

With the rapid development of evaluation datasets to assess LLMs understanding across a wide range of subjects and domains, identifying a suitable language understanding benchmark…

cs.CL2024

What explains the success of cross-modal fine-tuning with ORCA?

Paloma García-de-Herreros, Vagrant Gautam, Philipp Slusallek +2

ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning, i.e., applying pre-trained transformer models to modalities beyond their training data. The technique co…