55 citations · 109 across the 22 of their papers we have counts for
3 papers · 1 filter
All You Need is One: Capsule Prompt Tuning with a Single Vector
Yiyang Liu, James C. Liang, Heng Fan +7
Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning genera…
MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper
Runjia Zeng, Guangyan Sun, Qifan Wang +8
Considering deep neural networks as manifold mappers, the pretrain-then-fine-tune paradigm can be interpreted as a two-stage process: pretrain establishes a broad knowledge base, a…
Re-Imagining Multimodal Instruction Tuning: A Representation View
Yiyang Liu, James Chenhao Liang, Ruixiang Tang +8
Multimodal instruction tuning has proven to be an effective strategy for achieving zero-shot generalization by fine-tuning pre-trained Large Multimodal Models (LMMs) with instructi…