This paper introduces a Scandinavian benchmarking platform, ScandEval, which can
benchmark any pretrained model on four different tasks in the Scandinavian languages. The …

Machine learning (ML) datasets, often perceived as neutral, inherently encapsulate abstract
and disputed social constructs. Dataset curators frequently employ value-laden terms such …

Text generation is one of the most important and challenging tasks in NLP, where models
have shown a significant performance increase in recent years. However, most generative …

While Indic NLP has made rapid advances recently in terms of the availability of corpora and
pre-trained models, benchmark datasets on standard NLU tasks are limited. To this end, we …

Machine learning (ML) datasets, often perceived as" neutral," inherently encapsulate
abstract and disputed social constructs. Dataset curators frequently employ value-laden …

Machine-generated content reshapes the landscape of digital information; hence, ensuring
the authenticity of texts within digital libraries has become a paramount concern. This work …

The availability of compute and data to train larger and larger language models increases
the demand for robust methods of benchmarking the true progress of LM training. Recent …

In recent years, Large Language Models (LLMs) have achieved almost human-like
performance on various tasks. While some LLMs have been trained on multilingual data …

We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompts
for in-context learning (ICL). Inspired by adversarial learning, adv-ICL is implemented as a …

Running large-scale pre-trained language models in computationally constrained
environments remains a challenging problem yet to be addressed, while transfer learning …