[SUMMARY]HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
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Large language models (LLMs) have become increasingly capable, demonstrating strong performance on a variety of natural language processing tasks. However, LLMs are still narrow AI systems with limited capabilities. To expand the skills of LLMs, researchers at Anthropic proposed HuggingGPT, a system that uses LLMs to orchestrate many AI models. By planning and executing tasks through natural language, LLMs gain access to a breadth of capabilities.
HuggingGPT uses the OpenAI API to access the GPT-3 and Davinci models as its LLMs. It prompts these LLMs through natural language to plan, select, and execute AI tasks by utilizing models from Hugging Face's model hub. HuggingGPT breaks down abstract user requests into concrete tasks, matches expert models to each task, runs the models, and integrates the results into a summary response. For example, a request to "describe this image in detail" could be expanded into image captioning, classification, object detection, segmentation and visual question answering tasks. HuggingGPT would assign different models to each task, run them on the input image, and aggregate the results into a comprehensive description.
HuggingGPT has access to a breadth of capabilities by harnessing hundreds of models from Hugging Face. It can handle tasks across modalities including language, vision, audio and video. However, it faces some limitations. The inference time of large language models leads to high latency. Limited context lengths prevent tracking long conversational histories. And system instability can arise from unpredictable LLM outputs or unreliable model endpoints. But despite these limitations, HuggingGPT shows the promise of using LLMs as controllers to achieve complex, multi-modal AI.
HuggingGPT builds upon a line of work aimed at improving and scaling LLMs. Projects like Palm, Opt and LLAMA have developed methods for pre-training and tuning LLMs at massive scale. Researchers at Anthropic have also explored instruction tuning, generalization and reasoning in LLMs. And work on fusing vision and language, like BLIP, Visual ChatGPT and Toolformer, have enabled models with richer perceptual and language understanding.
Recent progress in natural language processing, computer vision and model scaling suggests many opportunities to improve HuggingGPT. Larger LLMs, vision-language models and model hubs will grant it access to more advanced capabilities. And prompting techniques can enable more complex planning, reasoning and generalization. Although still limited, HuggingGPT provides a framework for developing AI systems that utilize the strengths of both narrow expert models and broad generalist LLMs. By planning, executing and integrating information across models with complementary skills, systems like HuggingGPT could achieve artificial general intelligence.
Overall, HuggingGPT demonstrates how LLMs can gain access to a range of skills by orchestrating expert AI models. Using natural language as an interface, LLMs act as controllers that break down abstract tasks into concrete sub-tasks, match models to each sub-task, execute models and aggregate results. Despite facing efficiency, context length and stability limitations, HuggingGPT provides a promising approach for complex, multi-modal AI and a stepping stone towards artificial general intelligence. With recent progress in related fields, systems that integrate perception, language and reasoning at a massive scale may soon be within reach.
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