Large language models (LLMs) have been shown to be able to perform new tasks based on
a few demonstrations or natural language instructions. While these capabilities have led to …

We introduce C-Pack, a package of resources that significantly advance the field of general
Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a …

Today's trend of building ever larger language models (LLMs), while pushing the
performance of natural language processing, adds significant latency to the inference stage …

We present results from a large-scale experiment on pretraining encoders with non-
embedding parameter counts ranging from 700M to 9.3 B, their subsequent distillation into …

Abstract Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness
and security requirements driven by an increased number of AI-based systems. Existing …

Pretrained large language models (LLMs) have consistently shown state-of-the-art
performance across multiple natural language processing (NLP) tasks. These models are of …

Creating high-quality annotated data for task-oriented dialog (ToD) is known to be
notoriously difficult, and the challenges are amplified when the goal is to create equitable …

Building Natural Language Understanding (NLU) capabilities for Indic languages, which
have a collective speaker base of more than one billion speakers is absolutely crucial. In this …

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-
trained model in one modality is used for representation learning in another domain using …

Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …