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cs.CL2024
To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language Models
Junyan Lin, Haoran Chen, Dawei Zhu +1
In recent years, multimodal large language models (MLLMs) have garnered significant attention from both industry and academia. However, there is still considerable debate on constr…
cs.CL2024
Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
Chensen Huang, Guibo Zhu, Xuepeng Wang +5
To extend the context length of Transformer-based large language models (LLMs) and improve comprehension capabilities, we often face limitations due to computational resources and…