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Foundational models have demonstrated exceptional performance on established academic benchmarks, often narrowing the gap between human reasoning and artificial intelligence. These models have seamlessly integrated into our daily lives, enabling complex reasoning across domains such as personalized education systems, legal analyses, and scientific discovery. While the success of these models is widely attributed to their scale—encompassing both their architectural parameters and the vast pretraining data—the critical role of pretraining data in shaping their capabilities and limitations is often acknowledged but rarely studied. This is largely due to the large scale and unstructured nature of pretraining datasets, which pose challenges for analyzing and understanding their impact systematically. However, if we cannot disentangle model behavior from their pretraining data, how can we trust these systems in real-world, high-stakes applications?
In this thesis, we argue that understanding the true performance of foundational models requires going beyond conventional benchmark testing. In particular, incorporating insights from their pretraining data is essential for comprehensively evaluating and interpreting the models’ capabilities and limitations.
We focus on large language models (LLMs) and show that while LLMs often excel in benchmark settings, they can fail on basic, trivial reasoning tasks, raising concerns about their true robustness. To better understand these limitations, we examine the relationship between a model’s successes and failures through the lens of its pretraining data. We present methodologies for studying how pretraining data impacts a model’s reasoning performance and introduce Snoopy, a tool designed to facilitate such studies by analyzing the impact of term frequencies on model performance across various tasks. The final part of this thesis focuses on evaluating recent popular multimodal models in the context of chart reasoning. We leverage the understanding gained from earlier analyses to probe these models’ abilities in the task of reasoning with charts. Our findings reveal the limitations of foundational models, particularly their tendency to excel on benchmarks while struggling with fundamental reasoning tasks. By examining how pretraining data impacts model behavior, we emphasize the need for deeper, more granular evaluations to better interpret model performance and capabilities.