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The Impact of Model Compression on Subgroup Robustness in BERT Language Models
Introduction
The demand for large language models (LLMs) has led to the exploration of compression techniques to reduce model size and computational needs without compromising performance. This has significant implications for Natural Language Processing (NLP) applications, from document classification to conversational agents.
Research Overview
A research team from the University of Sussex, BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning, Monash University, and expert.ai conducted a comprehensive investigation into the effects of model compression on the subgroup robustness of BERT language models. The study explored 18 different compression methods using MultiNLI, CivilComments, and SCOTUS datasets.
Methodology and Findings
The study employed Empirical Risk Minimization (ERM) to train compressed BERT models and evaluated their efficacy using metrics like average accuracy and worst-group accuracy (WGA). Significant variances in model performance across different compression techniques were observed, highlighting the nuanced impacts of model compression on subgroup robustness.
Conclusion
The research sheds light on the impacts of model compression techniques on the robustness of BERT models towards minority subgroups across multiple datasets. It emphasizes that compression methods can improve model performance on minority subgroups, but effectiveness varies depending on the dataset and weight initialization after compression.
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