In the quest for artificial general intelligence (AGI), solving complicated AI tasks across diverse domains and modalities represents a significant milestone. As AI systems grow in scale, they often exhibit exponential performance improvements, opening new avenues for tackling increasingly complex tasks. That said, whether we can follow scaling laws indefinitely remains a matter of debate.
All large language models lack some capabilities (such as calculation) that are easy for narrow AI systems (such as calculators). But humans also lag behind calculators in calculation ability, and we solve that by… letting humans use calculators. Why not do the same for large language models? Enter HuggingGPT, a versatile natural language processing (NLP) framework based on the GPT family of models. It leverages the collective intelligence of LLMs and AI model repositories to scale its capabilities and achieve fairly remarkable results.
How does HuggingGPT compare with ChatGPT and other GPT models? More broadly, what tasks are inside the frontier of automation when we use all the tools at our disposal?
Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously. Considering large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks, with language serving as a generic interface to empower this. Based on this philosophy, we present HuggingGPT, an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence.