Information extraction from the scientific literature is a long-standing technique for transforming unstructured knowledge hidden in text into structured data, which can then be used for further analytics and decision-making in downstream tasks. A large body of scientific literature discusses Trust in AI, where factors contributing to human trust in artificial intelligence (AI) applications and technology are studied. It explores questions such as why people may or may not trust a self-driving car, and what factors influence such trust. The relationships of these factors with human trust in AI applications are complex. We explore this space through the lens of information extraction. That is, we investigate how to extract these factors from the literature that studies them. The outcome could inform technology developers to improve the acceptance rate of their products. Our results indicate that (1) while NER is largely considered a solved problem in many domains, it is far from solved in extracting factors of human trust in AI from the relevant scientific literature; and, (2) supervised learning is more effective for this task than prompt-based LLMs.

While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexplored. We investigate whether LLMs perceive the passage of time and adapt their decision-making accordingly through three complementary experiments. First, we introduce the Token-Time Hypothesis, positing that LLMs can map discrete token counts to continuous wall-clock time, and validate this through a dialogue duration judgment task. Second, we demonstrate that LLMs could use this awareness to adapt their response length while maintaining accuracy when users express urgency in question answering tasks. Finally, we develop BombRush, an interactive navigation challenge that examines how LLMs modify behavior under progressive time pressure in dynamic environments. Our findings indicate that LLMs possess certain awareness of time passage, enabling them to bridge discrete linguistic tokens and continuous physical time, though this capability varies with model size and reasoning abilities. This work establishes a theoretical foundation for enhancing temporal awareness in LLMs for time-sensitive applications.