in-context-learning

17 proyectos comparten este topic de GitHub

Las líneas conectan a los miembros que están mediblemente relacionados entre sí. El tamaño de los puntos refleja las estrellas.

🧬 Miembros

Awesome-Multimodal-Large-Language-Models

:sparkles::sparkles:Latest Advances on Multimodal Large Language Models

★ 18k

LLMSurvey

The official GitHub page for the survey paper "A Survey of Large Language Models".

★ 12.2k

Prompt_Engineering

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to…

★ 7.8k

open_flamingo

An open-source framework for training large multimodal models.

★ 4.1k

DecryptPrompt

总结Prompt&LLM论文，开源数据&模型，AIGC应用

★ 3.4k

LLMAgentPapers

Must-read Papers on LLM Agents.

★ 3.1k

prompt-in-context-learning

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3,…

★ 2.2k

vall-e

PyTorch implementation of VALL-E(Zero-Shot Text-To-Speech), Reproduced Demo…

★ 2.2k

Awesome-LLM4IE-Papers

Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)

★ 1.1k

Awesome-LLM-Uncertainty-Reliability-Robustness

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

★ 834

LM-reasoning

This repository contains a collection of papers and resources on Reasoning in Large Language Models.

★ 570

awesome-instruction-learning

Papers and Datasets on Instruction Tuning and Following. ✨✨✨

★ 511

synalinks

From idea to production in just few lines: Graph-Based Programmable Neuro-Symbolic LM Framework - a…

★ 446

EasyInstruct

[ACL 2024] An Easy-to-use Instruction Processing Framework for LLMs.

★ 407

freshqa

Data and code for FreshLLMs (https://arxiv.org/abs/2310.03214)

★ 402

Awesome-Multimodal-LLM

Research Trends in LLM-guided Multimodal Learning.

★ 355

Binder

[ICLR 2023] Code for the paper "Binding Language Models in Symbolic Languages"

★ 325

Medido a partir de los temas de GitHub compartidos por ambos proyectos, ponderado por cuán raros son cada uno de los temas.