Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Transformer-based pretrained language models (T-PTLMs) have achieved great success in
almost every NLP task. The evolution of these models started with GPT and BERT. These …

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for
Indonesian languages, including opening access to previously non-public resources …

Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

We present the findings of SemEval-2022 Task 11 on Multilingual Complex Named Entity
Recognition MULTICONER. Divided into 13 tracks, the task focused on methods to identify …

To develop high-performance natural language understanding (NLU) models, it is
necessary to have a benchmark to evaluate and analyze NLU ability from various …

General-purpose language models have demonstrated impressive capabilities, performing
on par with state-of-the-art approaches on a range of downstream natural language …

The BLOOM model is a large publicly available multilingual language model, but its
pretraining was limited to 46 languages. To extend the benefits of BLOOM to other …

GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs)
trained on hundreds of billion scale data. Here we address some remaining issues less …

Many recent studies on large-scale language models have reported successful in-context
zero-and few-shot learning ability. However, the in-depth analysis of when in-context …