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 …

Natural language generation has witnessed significant advancements due to the training of
large language models on vast internet-scale datasets. Despite these advancements, there …

Abstract Language models (LMs) like GPT‐3, PaLM, and ChatGPT are the foundation for
almost all major language technologies, but their capabilities, limitations, and risks are not …

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

Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from
various LMs. However, concerns including transparency, controllability, and affordability …

Comprehensive and accurate evaluation of general-purpose AI systems such as large
language models allows for effective mitigation of their risks and deepened understanding of …

Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting
with visual content with myriad potential downstream tasks. However, even though a list of …

We present Dolphin, a novel benchmark that addresses the need for a natural language
generation (NLG) evaluation framework dedicated to the wide collection of Arabic …

The ability to compare the semantic similarity between text corpora is important in a variety
of natural language processing applications. However, standard methods for evaluating …

In the evolving NLP landscape, benchmarks serve as yardsticks for gauging progress.
However, existing Legal NLP benchmarks only focus on predictive tasks, overlooking …