NoteLLM-2: Multimodal Large Representation Models for Recommendation
<|reference_start|>NoteLLM-2: Multimodal Large Representation Models for Recommendation: Large Language Models (LLMs) have demonstrated exceptional text understanding. Existing works explore their application in text embedding tasks. However, there are few works utilizing LLMs to assist multimodal representation tasks. In this work, we investigate the potential of LLMs to enhance multimodal representation in multimodal item-to-item (I2I) recommendations. One feasible method is the transfer of Multimodal Large Language Models (MLLMs) for representation tasks. However, pre-training MLLMs usually requires collecting high-quality, web-scale multimodal data, resulting in complex training procedures and high costs. This leads the community to rely heavily on open-source MLLMs, hindering customized training for representation scenarios. Therefore, we aim to design an end-to-end training method that customizes the integration of any existing LLMs and vision encoders to construct efficient multimodal representation models. Preliminary experiments show that fine-tuned LLMs in this end-to-end method tend to overlook image content. To overcome this challenge, we propose a novel training framework, NoteLLM-2, specifically designed for multimodal representation. We propose two ways to enhance the focus on visual information. The first method is based on the prompt viewpoint, which separates multimodal content into visual content and textual content. NoteLLM-2 adopts the multimodal In-Content Learning method to teach LLMs to focus on both modalities and aggregate key information. The second method is from the model architecture, utilizing a late fusion mechanism to directly fuse visual information into textual information. Extensive experiments have been conducted to validate the effectiveness of our method.<|reference_end|>
@article{zhang2024notellm-2:,
    title={NoteLLM-2: Multimodal Large Representation Models for Recommendation},
    author={Chao Zhang, Haoxin Zhang, Shiwei Wu, Di Wu, Tong Xu, Yan Gao, Yao Hu,
  Enhong Chen},
    journal={arXiv preprint arXiv:2405.16789},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16789},
    primaryClass={cs.IR}
}