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 …

Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model
with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

Contemporary works on abstractive text summarization have focused primarily on high-
resource languages like English, mostly due to the limited availability of datasets for low/mid …

Studying data memorization in neural language models helps us understand the risks (eg, to
privacy or copyright) associated with models regurgitating training data and aids in the …

We introduce GEM, a living benchmark for natural language Generation (NLG), its
Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving …

We introduce MARGE, a pre-trained sequence-to-sequence model learned with an
unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an …

This paper presents the overview of the AuTexTification shared task as part of the IberLEF
2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …