⭐️ Ranking LLMs with Elo Ratings
Choosing an LLM from 50+ models available today is hard. We explore Elo ratings as a method to objectively rank and pick the best performers for our use case.
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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
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