AI Breakdown

arxiv preprint - SpaceByte: Towards Deleting Tokenization from Large Language Modeling

In this episode, we discuss SpaceByte: Towards Deleting Tokenization from Large Language Modeling by Kevin Slagle. Tokenization in large language models, while improving performance, presents challenges such as bias, increased adversarial vulnerability, and complexity. The new byte-level decoder architecture, SpaceByte, significantly diminishes these issues by integrating larger transformer blocks selectively at critical bytes like spaces, improving model performance on a fixed computational budget. SpaceByte's approach allows it to outperform other byte-level models and rival the effectiveness of subword-based Transformer models.

arxiv preprint - TextSquare: Scaling up Text-Centric Visual Instruction Tuning

In this episode, we discuss TextSquare: Scaling up Text-Centric Visual Instruction Tuning by Jingqun Tang, Chunhui Lin, Zhen Zhao, Shu Wei, Binghong Wu, Qi Liu, Hao Feng, Yang Li, Siqi Wang, Lei Liao, Wei Shi, Yuliang Liu, Hao Liu, Yuan Xie, Xiang Bai, Can Huang. The paper describes advancements in text-centric visual question answering using a novel dataset called Square-10M, developed to improve Multimodal Large Language Models (MLLMs) through instruction tuning. The dataset, generated with closed-source MLLMs, employs a method named Square that covers Self-Questioning, Answering, Reasoning, and Evaluation for data construction. Experiments on the dataset indicated significant performance enhancements over existing models, highlighting the importance of the quantity of reasoning data in VQA for enhancing accuracy and reducing errors in model responses.

arxiv preprint - EdgeFusion: On-Device Text-to-Image Generation

In this episode, we discuss EdgeFusion: On-Device Text-to-Image Generation by Thibault Castells, Hyoung-Kyu Song, Tairen Piao, Shinkook Choi, Bo-Kyeong Kim, Hanyoung Yim, Changgwun Lee, Jae Gon Kim, Tae-Ho Kim. The paper "EdgeFusion: On-Device Text-to-Image Generation" explores the difficulties of using Stable Diffusion models in text-to-image generation due to their intensive computational needs. It proposes a new, more efficient model based on a condensed version of Stable Diffusion, which incorporates novel strategies utilizing high-quality image-text pairs and an optimized distillation process specifically suited for the Latent Consistency Model. Their approach results in the ability to quickly generate high-quality, contextually accurate images on low-resource devices, achieving performance under one second per image generation.

arxiv preprint - VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

In this episode, we discuss VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time by Sicheng Xu, Guojun Chen, Yu-Xiao Guo, Jiaolong Yang, Chong Li, Zhenyu Zang, Yizhong Zhang, Xin Tong, Baining Guo. VASA is a new framework designed to create realistic talking faces from a static image and audio clip, featuring lip synchronization, facial expressions, and head movements. It utilizes a diffusion-based model in a face latent space for generating dynamic facial and head movements, improving the authenticity and liveliness of the avatars. VASA-1 delivers high-quality, real-time video generation at up to 40 FPS, outperforming existing technologies in realism and responsiveness, suitable for real-time avatar interaction. Project page: https://www.microsoft.com/en-us/research/project/vasa-1/

arxiv preprint - Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

In this episode, we discuss Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models by Yanwei Li, Yuechen Zhang, Chengyao Wang, Zhisheng Zhong, Yixin Chen, Ruihang Chu, Shaoteng Liu, Jiaya Jia. The paper introduces Mini-Gemini, a framework aimed at improving Vision Language Models (VLMs) by addressing the performance gap with advanced models like GPT-4. Mini-Gemini focuses on three main enhancements: incorporating high-resolution visual tokens without added computational cost, creating a high-quality dataset for refined image understanding and reasoning, and facilitating VLMs to support diverse tasks such as image understanding and generation simultaneously. The framework, compatible with various large language models ranging from 2B to 34B parameters, has shown superior performance in zero-shot benchmarks and is available for public use. Project page: https://mini-gemini.github.io/

arxiv preprint - High-Dimension Human Value Representation in Large Language Models

In this episode, we discuss High-Dimension Human Value Representation in Large Language Models by Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, Pascale Fung. The paper addresses the importance of aligning large language models (LLMs) with human values, introducing a new method called UniVaR for representing human value distributions within these models. UniVaR, which is independent of model architecture and training data, has been applied to eight multilingual LLMs and tested on four distinct LLMs to compare the embedded value distributions. The findings show that UniVaR can illuminate the variation in human values across different languages and cultures within various LLMs.

arxiv preprint - Why do small language models underperform? Studying Language Model Saturation via the Softmax Bottleneck

In this episode, we discuss Why do small language models underperform? Studying Language Model Saturation via the Softmax Bottleneck by Nathan Godey, Éric de la Clergerie, Benoît Sagot. This paper investigates the phenomenon of performance saturation in small language models, attributing the issue to a mismatch between the model's hidden layer size and the complexity of the targeted probability distribution. The softmax bottleneck, a known limitation in neural networks, is identified as a contributing factor to this mismatch, leading to suboptimal performance due to the emergence of degenerate latent representations during late pretraining stages. The study demonstrates that models with fewer than 1000 hidden dimensions are particularly susceptible to this effect, resulting in decreased effectiveness upon evaluation.

arxiv preprint - Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

In this episode, we discuss Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention by Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal. The paper presents a novel method for enabling Transformer-based Large Language Models to process extremely long inputs while keeping memory and computational requirements fixed. The technique introduced, called Infini-attention, blends a new form of memory-augmented attention with local and linear long-term attention within a single Transformer layer. The effectiveness of this method is demonstrated through impressive performance on long-context challenges, including a one million length sequence task and a half-million word book summarization, while maintaining efficient streaming capabilities and a minimal increase in memory parameters.

arxiv preprint - Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

In this episode, we discuss Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs by Keen You, Haotian Zhang, Eldon Schoop, Floris Weers, Amanda Swearngin, Jeffrey Nichols, Yinfei Yang, Zhe Gan. The paper presents Ferret-UI, a new multimodal large language model tailored for interpreting and interacting with mobile user interface screens, which overcomes common challenges through a novel approach of dividing screens into sub-images for enhanced detail processing. The model has been trained on a variety of UI-focused tasks with improved instruction-following and region annotations, enhancing its abilities in tasks like icon recognition and conversational interaction. Ferret-UI demonstrates superior performance in UI comprehension and task execution compared to existing models, establishing a new benchmark for the evaluation of MLLMs in the context of user interface understanding.

arxiv preprint - Evaluating Text-to-Visual Generation with Image-to-Text Generation

In this episode, we discuss Evaluating Text-to-Visual Generation with Image-to-Text Generation by Zhiqiu Lin, Deepak Pathak, Baiqi Li, Jiayao Li, Xide Xia, Graham Neubig, Pengchuan Zhang, Deva Ramanan. The paper introduces VQAScore, a novel metric for evaluating the alignment of generated images to text prompts, utilizing a visual-question-answering model to score the relevance of images to prompts based on a simple yes-or-no question. Unlike existing metrics, the proposed VQAScore effectively handles complex prompts, demonstrating superior performance across numerous benchmarks, even when compared to proprietary models like GPT-4V. Additionally, the paper presents GenAI-Bench, a challenging new benchmark consisting of compositional text prompts and human ratings, and provides open-source access to their data and models to facilitate further research in text-to-visual generation evaluations.