collaborators

12 papers

cs.CL2026

Challenges and Recommendations for LLMs-as-a-Judge in Multilingual Settings and Low-Resource Languages

A. Seza Doğruöz, Xixian Liao, Verena Blaschke +3

LLM-as-a-Judge has become the dominant evaluation paradigm for many natural language generation tasks, due to shortcomings of conventional metrics and high correlations with human…

cs.CL2026

An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages

Yinhan Lu, Gaganpreet Jhajj, Chen Zhang +2

In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent…

cs.CL2026

TukaBench: A Culturally Grounded Jailbreak Benchmark for African Languages

Victor Akinode, Senyu Li, Wassim Hamidouche +3

Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly African ones, critically underexplored. We in…

cs.CL2026

Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures

Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377

To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…

cs.CL2026

AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation

Idris Abdulmumin, Tajuddeen Gwadabe, Shamsuddeen Hassan Muhammad +11

The dominance of colonial languages in African education and scientific communication limits how hundreds of millions of speakers of African languages access and produce scientific…

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…