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 …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

This paper proposes a framework for quantitatively evaluating interactive LLMs such as
ChatGPT using publicly available data sets. We carry out an extensive technical evaluation …

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

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

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

Transfer learning is a technique utilized in deep learning applications to transmit learned
inference to a different target domain. The approach is mainly to solve the problem of a few …

NLP research is impeded by a lack of resources and awareness of the challenges presented
by underrepresented languages and dialects. Focusing on the languages spoken in …

Object hallucination poses a significant challenge in vision-language (VL) models, often
leading to the generation of nonsensical or unfaithful responses with non-existent objects …

Recent advances in the pre-training of language models leverage large-scale datasets to
create multilingual models. However, low-resource languages are mostly left out in these …