Fuzzy fingerprinting large pre-trained models

摘要: Large pre-trained models like BERT and RoBERTa have gained massive popularity as they have surpassed previous state-of-the-art models in various Natural Language Processing (NLP) tasks. Nevertheless, interpreting their behavior is still an ongoing challenge as these models are composed of millions of parameters. The introduction of the Fuzzy Fingerprint (FFP) framework provided a straightforward classification technique able to deliver result interpretations, however, this method was outperformed by these large pre-trained models. In this work, we introduce a novel method that combines the simplicity of the FFPs with the ability to detect complex patterns of large pre-trained models, in order to build a more interpretable classification framework. Furthermore, we show that it is feasible to obtain unique FFPs for each label that enable the examination of incorrect classifications. We evaluate our new method on …