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Collections including paper arxiv:2404.04125

AI Paper of the Day

A collection of papers that I think are interesting, one added each day

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 20
OLMo: Accelerating the Science of Language Models

Paper • 2402.00838 • Published Feb 1 • 76
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity

Paper • 2401.17072 • Published Jan 30 • 22

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27

Vision and language

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models

Paper • 2404.02747 • Published Apr 3 • 11
3D Congealing: 3D-Aware Image Alignment in the Wild

Paper • 2404.02125 • Published Apr 2 • 6

Foundation Models

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies

Paper • 2404.08197 • Published Apr 12 • 26
Probing the 3D Awareness of Visual Foundation Models

Paper • 2404.08636 • Published Apr 12 • 11
AM-RADIO: Agglomerative Model -- Reduce All Domains Into One

Paper • 2312.06709 • Published Dec 10, 2023 • 1

Papers - University of Tubingen

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models

Paper • 2404.06209 • Published Apr 9 • 4

Papers - University of Oxford

Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

Paper • 1705.04146 • Published May 11, 2017 • 1
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?

Paper • 2404.03411 • Published Apr 4 • 8
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27
Hydragen: High-Throughput LLM Inference with Shared Prefixes

Paper • 2402.05099 • Published Feb 7 • 17

Papers - University of Cambridge

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Paper • 2309.12284 • Published Sep 21, 2023 • 16
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27

Multimodal Embeddings

MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions

Paper • 2403.19651 • Published Mar 28 • 22
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies

Paper • 2404.08197 • Published Apr 12 • 26
Gecko: Versatile Text Embeddings Distilled from Large Language Models

Paper • 2403.20327 • Published Mar 29 • 47

Papers - Google

Lumiere: A Space-Time Diffusion Model for Video Generation

Paper • 2401.12945 • Published Jan 23 • 83
Long-form factuality in large language models

Paper • 2403.18802 • Published Mar 27 • 23
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion

Paper • 2403.18818 • Published Mar 27 • 22
TC4D: Trajectory-Conditioned Text-to-4D Generation

Paper • 2403.17920 • Published Mar 26 • 15

Papers - Multimodal - Report

significant improvements in zero-shot performance require exponentially more data, following a log-linear scaling trend

Qwen Technical Report

Paper • 2309.16609 • Published Sep 28, 2023 • 30
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27

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