Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

The multimodal task of Visual Question Answering (VQA) encompassing elements of
Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers …

Reliable evaluation benchmarks designed for replicability and comprehensiveness have
driven progress in machine learning. Due to the lack of a multilingual benchmark, however …

PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m
vision encoder and the Gemma-2B language model. It is trained to be a versatile and …

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 …

Recently there has been a significant surge in multimodal learning in terms of both image-to-
text and text-to-image generation. However, the success is typically limited to English …

We introduce Adapters, an open-source library that unifies parameter-efficient and modular
transfer learning in large language models. By integrating 10 diverse adapter methods into a …

Abstract Multilingual Vision-Language Pre-training (VLP) is a promising but challenging
topic due to the lack of large-scale multilingual image-text pairs. Existing works address the …