Lion Optimizer's Impact on ModernBERT Cross-Encoder Robustness Against Domain Shift

Language models have revolutionized natural language processing, becoming an integral part of many applications. However, these models often exhibit societal biases embedded in their training data, raising concerns about their fairness and ethical deployment. Measuring these biases usually requires creating datasets with time-consuming human annotation, which is costly and hard to expand. To address this challenge, we propose a data curation framework and CineBias, a novel dataset of 1,012 stereotypical sentence pairs covering seven bias categories, extracted from Hollywood movie subtitles wit

Research goal: Does the Lion optimizer improve the robustness of ModernBERT cross-encoders against domain shift when evaluated on out-of-distribution retrieval benchmarks beyond MS MARCO?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.