3 citations · 7 across the 11 of their papers we have counts for
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cs.CL2023★ 3 cited
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper
Chengyu Wang, Junbing Yan, Wei Zhang +1
This paper delves into the pressing need in Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs). While LLMs possess remarkable capabilities, their extensive par…
cs.CL2023★ 1 cited
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Junbing Yan, Chengyu Wang, Taolin Zhang +3
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps. Yet, complex logical reasoning…
cs.CL2023
Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters
Yukang Xie, Chengyu Wang, Junbing Yan +3
Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generativ…