LLMと人間の心の構造的類似性を解明する認知科学論考

LLMと人間の心は「認知的な"いとこ”」であり、基盤は違えど、同じ計算的家族の一員ではないか、と主張...

一晩の脳波から神経変性疾患リスクを予測するAgentic AI

"一晩"の「睡眠中の脳波」をAIで調べて、その人が将来パーキンソン病やアルツハイマー病になるリスクを...

LLMと音声分析で初対面の好意を予測するAI手法

婚活パーティーのような場での会話をAIに分析させると、「相手をどれくらい好きになったか」がある程度当...

GPT-4 Technical Report

OpenAI

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks.

Diffusion Models Beat GANs on Image Synthesis

Prafulla Dhariwal, Alex Nichol

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations.

LoRA: Low-Rank Adaptation of Large Language Models

Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen

We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.