Imagen 3

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

RETSim: Resilient and Efficient Text Similarity

This paper introduces RETSim (Resilient and Efficient Text Similarity), a lightweight, multilingual deep learning model trained to produce robust metric embeddings for near-duplicate text retrieval, clustering, and dataset deduplication tasks. We demonstrate that RETSim is significantly more robust and accurate than MinHash and neural text embeddings, achieving new state-of-the-art performance on dataset deduplication, adversarial text retrieval benchmarks, and spam clustering tasks. We also introduce the W4NT3D benchmark (Wiki-40B 4dversarial Near-T3xt Dataset) for evaluating multilingual, near-duplicate text retrieval capabilities under adversarial settings. RETSim and the W4NT3D benchmark are open-sourced under the MIT License at https://github.com/google/unisim.

RetVec: Resilient and Efficient Text Vectorizer

This paper describes RetVec, a resilient multilingual embedding scheme designed for neural-based text processing, including small-text classification and large-language models. RetVec combines a novel character encoding with an optional small model to embed words into a 256-dimensional vector space. These embeddings enable training competitive multilingual text models resilient to typos and adversarial attacks. In this paper, we evaluate and compare RetVec to state-of-the-art tokenizers and word embeddings on common model architectures. These comparisons demonstrate that RetVec leads to competitive models that are significantly more resilient to text perturbations across a variety of common tasks. RetVec is available under Apache 2 license at \url{https://github.com/[anonymized]}.