4 papers
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…
Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance
Anamika Paul Rupa, Anietie Andy
Recent attacks show that behavioural unlearning of large language models leaves internal traces recoverable by adversarial probes. We characterise where this retention lives and sh…
Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages
Oluwadara Kalejaiye, Luel Hagos Beyene, David Ifeoluwa Adelani +4
Nigeria is the most populous country in Africa with a population of more than 200 million people. More than 500 languages are spoken in Nigeria and it is one of the most linguistic…
Mitigating Translationese in Low-resource Languages: The Storyboard Approach
Garry Kuwanto, Eno-Abasi E. Urua, Priscilla Amondi Amuok +21
Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect…