Scaling Self-Supervised Objectives for Vision-Language Model Robustness in Domain Shift

Abstract

Abstract: Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, arc

Research Question

To what extent does scaling self-supervised objectives reduce the performance drop of vision-language models on reasoning tasks under domain shift conditions?

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