Paper Summaries

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

Arxiv URL: https://arxiv.org/abs/2303.17580v1

Authors: Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang

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 HuggingFace to cover numerous sophisticated AI tasks in different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards AGI.

Key Insights & Learnings:

LLMs have exhibited exceptional ability in language understanding, generation, interaction, and reasoning.
LLMs lack the ability to process complex information such as vision and speech.
LLMs should be able to coordinate with external models to utilize their powers.
Language is a generic interface for LLMs to connect AI models.
HuggingGPT integrates hundreds of models on HuggingFace around ChatGPT to tackle generalized AI tasks.

Terms Mentioned: large language models, ChatGPT, HuggingFace, AI tasks, natural language processing, LLaMa, zero-shot, chain-of-thought prompting, instruction tuning

Technologies / Libraries Mentioned: HuggingFace, Azure, nlpconnet/vit-gpt2-image-captioning, facebook/detr-resnet-101, google/vit

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

Are We Really Making Much Progress in Text Classification? A Comparative Review - Summary

This paper reviews and compares methods for single-label and multi-label text classification, categorizing them into bag-of-words, sequence-based, graph-based, and hierarchical methods. The findings reveal that pre-trained language models outperform all recently proposed graph-based and hierarchy-b

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

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