Plausibility Processing

Plausibility processing focuses on evaluating the likelihood or reasonability of events, statements, or designs, a crucial aspect of artificial intelligence and human-computer interaction. Current research investigates this through various approaches, including the development of datasets for annotating plausibility, the application of deep learning models like denoising autoencoders to assess the structural plausibility of generated designs, and the analysis of attention mechanisms within transformer language models to understand their internal plausibility judgments. This work is significant for improving the reliability and trustworthiness of AI systems, particularly in applications like natural language processing and autonomous driving, by mitigating errors stemming from implausible outputs or interpretations.