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Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks.
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DreamLLM: Synergistic Multimodal Comprehension and Creation. In The Twelfth International Conference on Learning Representations
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Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. 2023 · 2023
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Llm2vec: Large language models are secretly powerful text encoders
Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, and Siva Reddy. 2024 · 2024
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TOMGPT: Reliable Text-Only Training Approach for Cost-Effective Multi-modal Large Language Model
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DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
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Prismatic vlms: Investigating the design space of visually-conditioned language models
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MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
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Generative representational instruction tuning
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LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
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Repetition Improves Language Model Embeddings
Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, and Aditi Raghunathan. 2024 · 2024
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Quan Sun, Jinsheng Wang, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, and Xinlong Wang. 2024 · 2024
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Large language models for generative information extraction: A survey
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Long-clip: Unlocking the long-text capability of clip
Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, and Jiaqi Wang. 2024c · 2024
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NoteLLM: A Retrievable Large Language Model for Note Recommendation
Chao Zhang, Shiwei Wu, Haoxin Zhang, Tong Xu, Yan Gao, Yao Hu, and Enhong Chen. 2024a · 2024
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Debiasing Large Visual Language Models
Yi-Fan Zhang, Weichen Yu, Qingsong Wen, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. 2024b · 2024
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