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The Quill

HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace - Summary

The paper proposes HuggingGPT, a system that uses large language models (LLMs) like ChatGPT to connect various AI models in machine learning communities like HuggingFace to solve complicated AI tasks. The system leverages the strong language capability of ChatGPT and abundant AI models in HuggingFa

Sparks of Artificial General Intelligence: Early experiments with GPT-4 - Summary

The paper reports on the investigation of an early version of GPT-4, which is part of a new cohort of LLMs that exhibit more general intelligence than previous AI models. The paper demonstrates that GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psyc

A Survey of Large Language Models - Summary

This paper surveys the recent advances in Large Language Models (LLMs), which are pre-trained Transformer models over large-scale corpora. The paper discusses the background, key findings, and mainstream techniques of LLMs, focusing on pre-training, adaptation tuning, utilization, and capacity eval

Generative Agents: Interactive Simulacra of Human Behavior - Summary

The paper introduces generative agents, which are computational software that simulate believable human behavior. The agents can be used in interactive applications such as immersive environments, rehearsal spaces for interpersonal communication, and prototyping tools. The paper describes an archit

Segment Everything Everywhere All at Once - Summary

The paper presents SEEM, a promptable, interactive model for Segmenting Everything Everywhere all at once in an image. It introduces a versatile prompting engine for different types of prompts, including points, boxes, scribbles, masks, texts, and referred regions of another image. The model can ef

Eight Things to Know about Large Language Models - Summary

The paper discusses eight potentially surprising claims about large language models (LLMs), including their predictable increase in capability with increasing investment, the unpredictability of specific behaviors, and the lack of reliable techniques for steering their behavior.

Instruction Tuning with GPT-4 - Summary

The paper presents the first attempt to use GPT-4 to generate instruction-following data for Large Language Models (LLMs) finetuning. The 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks compared to the instruction-following