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The Quill

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

SegGPT: Segmenting Everything In Context - Summary

SegGPT is a generalist model for segmenting everything in context. It unifies various segmentation tasks into a generalist in-context learning framework that can perform arbitrary segmentation tasks in images or videos via in-context inference. It is evaluated on a broad range of tasks, including f

Generative Agents: Interactive Simulacra of Human Behavior - Summary

The paper introduces generative agents, which simulate believable human behavior and can be used in interactive applications. The agents are created using an architecture that extends a large language model to store and retrieve memories, and they produce believable individual and emergent social b