HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace - Summary

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