Genetic algorithms as an optimization strategy to reduce the complexity of fuzzy fingerprints from large language models

Large Language Models currently represent the forefront of Natural Language Processing for classification tasks. Recent studies have integrated Fuzzy Fingerprints as a novel classification layer within these models to enhance result interpretability and reduce overall model complexity, without substantially compromising performance. However, in more challenging settings, this framework requires a larger fingerprint size to achieve competitive results when compared to using the full classification model. In this work, we employ Genetic Algorithms to the Fuzzy Fingerprint framework to further optimize these fingerprints to surpass the performance of conventional large pre-trained classifiers, while also offering improvements in interpretability and reduced complexity. Our empirical analysis demonstrates that optimizing to a smaller fingerprint size not only improves interpretability but also, when compared to a baseline with a larger fingerprint size, delivers higher performance relative to leading-edge methodologies.