SwitchGPT: Adapting Large Language Models for Non-Text Outputs

• URL: http://arxiv.org/abs/2309.07623v1
• Date: Thu, 14 Sep 2023 11:38:23 GMT
• Title: SwitchGPT: Adapting Large Language Models for Non-Text Outputs
• Authors: Xinyu Wang, Bohan Zhuang, Qi Wu
• Abstract summary: Large Language Models (LLMs) are primarily trained on text-based datasets. LLMs exhibit exceptional proficiencies in understanding and executing complex linguistic instructions via text outputs. We propose a novel approach that evolves a text-based LLM into a multi-modal one.
• Score: 28.656227306028743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs), primarily trained on text-based datasets, exhibit exceptional proficiencies in understanding and executing complex linguistic instructions via text outputs. However, they falter when requests to generate non-text ones. Concurrently, modality conversion models, such as text-to-image, despite generating high-quality images, suffer from a lack of extensive textual pretraining. As a result, these models are only capable of accommodating specific image descriptions rather than comprehending more complex instructions. To bridge this gap, we propose a novel approach, \methodname, from a modality conversion perspective that evolves a text-based LLM into a multi-modal one. We specifically employ a minimal dataset to instruct LLMs to recognize the intended output modality as directed by the instructions. Consequently, the adapted LLM can effectively summon various off-the-shelf modality conversion models from the model zoos to generate non-text responses. This circumvents the necessity for complicated pretraining that typically requires immense quantities of paired multi-modal data, while simultaneously inheriting the extensive knowledge of LLMs and the ability of high-quality generative models. To evaluate and compare the adapted multi-modal LLM with its traditional counterparts, we have constructed a multi-modal instruction benchmark that solicits diverse modality outputs. The experiment results reveal that, with minimal training, LLMs can be conveniently adapted to comprehend requests for non-text responses, thus achieving higher flexibility in multi-modal scenarios. Code and data will be made available at https://github.com/xinke-wang/SwitchGPT.

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