6 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 1 cited
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
Guoxin Chen, Yiming Qian, Bowen Wang +1
The large language models have achieved superior performance on various natural language tasks. One major drawback of such approaches is they are resource-intensive in fine-tuning…
cs.CL2023★ 1 cited
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Junyi Liu, Liangzhi Li, Tong Xiang +2
Since ChatGPT released its API for public use, the number of applications built on top of commercial large language models (LLMs) increase exponentially. One popular usage of such…
cs.CL2023★ 6 cited
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
Feiyu Zhang, Liangzhi Li, Junhao Chen +3
With the increasing size of pre-trained language models (PLMs), fine-tuning all the parameters in the model is not efficient, especially when there are a large number of downstream…