This paper explores the potential of leveraging Large Language Models (LLMs) for data
augmentation in multilingual commonsense reasoning datasets where the available training …

Although recent Massively Multilingual Language Models (MMLMs) like mBERT and XLMR
support around 100 languages, most existing multilingual NLP benchmarks provide …

We consider the task of optimally fine-tuning pre-trained multilingual models, given small
amounts of unlabelled target data and an annotation budget. In this paper, we introduce …

Evaluating the performance of Massively Multilingual Language Models (MMLMs) is difficult
due to the shortage of evaluation datasets in low-resource languages. Due to computational …

Abstract Performance prediction for Natural Language Processing (NLP) seeks to reduce
the experimental burden resulting from the myriad of different evaluation scenarios, eg, the …

Abstract The SUMEval Workshop's shared task involved predicting performance of
multilingual PLMs across multiple languages when these models are fine-tuned with varying …

Scaling laws are predictable relations between the performance of AI systems and various
scalable design choices such as model or dataset size. In order to keep predictions …

This paper describes the submission of our multilingual NLP model performance evaluation
system for the SUMEval 2022 shared task, a system for predict the performance of a model …

The majority of previous researches addressing multi-lingual IE are limited to zero-shot
cross-lingual single-transfer (one-to-one) setting, with high-resource languages …