The Trade-off between Performance, Efficiency, and Fairness in Adapter Modules for Text Classification

arXiv:2405.02010v1 Announce Type: new
Abstract: Current natural language processing (NLP) research tends to focus on only one or, less frequently, two dimensions - e.g., performance, privacy, fairness, or efficiency - at a time, which may lead to suboptimal conclusions and often overlooking the broader goal of achieving trustworthy NLP. Work on adapter modules (Houlsby et al., 2019; Hu et al., 2021) focuses on improving performance and efficiency, with no investigation of unintended consequences on other aspects such as fairness. To …

abstract adapter arxiv classification cs.cl current dimensions efficiency fairness focus language language processing modules natural natural language natural language processing nlp performance privacy processing research text text classification trade trade-off type