PILL: Plug Into LLM with Adapter Expert and Attention Gate

• URL: http://arxiv.org/abs/2311.02126v1
• Date: Fri, 3 Nov 2023 09:31:10 GMT
• Title: PILL: Plug Into LLM with Adapter Expert and Attention Gate
• Authors: Fangyuan Zhang, Tingting Liang, Zhengyuan Wu, Yuyu Yin
• Abstract summary: We introduce a novel architecture called PILL: Plug Into LLM with adapter expert and attention gate. We introduce two modules: Firstly, utilizing Mixture-of-Modality-Adapter-Expert to independently handle different modalities. Secondly, by introducing Modality-Attention-Gating, which enables adaptive control of the contribution of modality tokens to the overall representation.
• Score: 11.956931222769128
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Due to the remarkable capabilities of powerful Large Language Models (LLMs) in effectively following instructions, there has been a growing number of assistants in the community to assist humans. Recently, significant progress has been made in the development of Vision Language Models (VLMs), expanding the capabilities of LLMs and enabling them to execute more diverse instructions. However, it is foreseeable that models will likely need to handle tasks involving additional modalities such as speech, video, and others. This poses a particularly prominent challenge of dealing with the complexity of mixed modalities. To address this, we introduce a novel architecture called PILL: Plug Into LLM with adapter expert and attention gate to better decouple these complex modalities and leverage efficient fine-tuning. We introduce two modules: Firstly, utilizing Mixture-of-Modality-Adapter-Expert to independently handle different modalities, enabling better adaptation to downstream tasks while preserving the expressive capability of the original model. Secondly, by introducing Modality-Attention-Gating, which enables adaptive control of the contribution of modality tokens to the overall representation. In addition, we have made improvements to the Adapter to enhance its learning and expressive capabilities. Experimental results demonstrate that our approach exhibits competitive performance compared to other mainstream methods for modality fusion. For researchers interested in our work, we provide free access to the code and models at https://github.com/DsaltYfish/PILL.

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