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cs.CL2024

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Petko Georgiev, Ving Ian Lei +1132

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…

cs.CV2024

Benchmarking Vision Language Models for Cultural Understanding

Shravan Nayak, Kanishk Jain, Rabiul Awal +5

Foundation models and vision-language pre-training have notably advanced Vision Language Models (VLMs), enabling multimodal processing of visual and linguistic data. However, their…

cs.CV2024

VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models

Harshit, Tolga Tasdizen

The recent developments in deep learning led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated Vision and Language Mode…

cs.CY2024

The Ethics of Advanced AI Assistants

Iason Gabriel, Arianna Manzini, Geoff Keeling +54

This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural langu…

cs.AI2024

Holistic Safety and Responsibility Evaluations of Advanced AI Models

Laura Weidinger, Joslyn Barnhart, Jenny Brennan +16

Safety and responsibility evaluations of advanced AI models are a critical but developing field of research and practice. In the development of Google DeepMind's advanced AI models…