A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt

Y Cao, S Li, Y Liu, Z Yan, Y Dai, PS Yu… - arXiv preprint arXiv …, 2023 - arxiv.org
Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

A comprehensive overview of large language models

H Naveed, AU Khan, S Qiu, M Saqib, S Anwar… - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

A survey of large language models

WX Zhao, K Zhou, J Li, T Tang, X Wang, Y Hou… - arXiv preprint arXiv …, 2023 - arxiv.org
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

The flan collection: Designing data and methods for effective instruction tuning

S Longpre, L Hou, T Vu, A Webson… - International …, 2023 - proceedings.mlr.press
We study the design decision of publicly available instruction tuning methods, by
reproducing and breaking down the development of Flan 2022 (Chung et al., 2022) …

Alpacafarm: A simulation framework for methods that learn from human feedback

Y Dubois, CX Li, R Taori, T Zhang… - Advances in …, 2024 - proceedings.neurips.cc
Large language models (LLMs) such as ChatGPT have seen widespread adoption due to
their ability to follow user instructions well. Developing these LLMs involves a complex yet …

Qwen technical report

J Bai, S Bai, Y Chu, Z Cui, K Dang, X Deng… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …

Wizardlm: Empowering large language models to follow complex instructions

C Xu, Q Sun, K Zheng, X Geng, P Zhao, J Feng… - arXiv preprint arXiv …, 2023 - arxiv.org
Training large language models (LLMs) with open-domain instruction following data brings
colossal success. However, manually creating such instruction data is very time-consuming …

Galactica: A large language model for science

R Taylor, M Kardas, G Cucurull, T Scialom… - arXiv preprint arXiv …, 2022 - arxiv.org
Information overload is a major obstacle to scientific progress. The explosive growth in
scientific literature and data has made it ever harder to discover useful insights in a large …

Crosslingual generalization through multitask finetuning

N Muennighoff, T Wang, L Sutawika, A Roberts… - arXiv preprint arXiv …, 2022 - arxiv.org
Multitask prompted finetuning (MTF) has been shown to help large language models
generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused …

Wizardcoder: Empowering code large language models with evol-instruct

Z Luo, C Xu, P Zhao, Q Sun, X Geng, W Hu… - arXiv preprint arXiv …, 2023 - arxiv.org
Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated
exceptional performance in code-related tasks. However, most existing models are solely …