HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation
Year: 2023

Abstract:
To fully leverage the advantages of large-scale pre-trained language models (PLMs) on downstream tasks, it has become a ubiquitous adaptation paradigm to fine-tune the entire parameters of PLMs. However, this paradigm poses issues of inefficient updating and resource overconsuming for fine-tuning in data-scarce and resource-limited scenarios, because of the large scale of parameters in PLMs. To alleviate these concerns, in this paper, we propose a parameterefficient fine-tuning method HiFi, that is, only the highly informative and strongly correlated attention heads for the specific task are finetuned. To search for those significant attention heads, we develop a novel framework to analyze the effectiveness of heads. Specifically, we first model the relationship between heads into a graph from two perspectives of information richness and correlation, and then apply PageRank algorithm to determine the relative importance of each head. Extensive experiments on the GLUE benchmark demonstrate the effectiveness of our method, and show that HiFi obtains state-of-the-art performance over the prior baselines.

heading: Future Work
text: In future work, we will continue to explore how to design better selection strategies (e. g. , an intuitive idea is that k should be layer-specific). (ii) So far, the models and datasets we tested all belong to NLP. Due to time and resource limitations, we do not evaluate the related models and datasets (e. g. , ViT, CIFAR) in CV