Structural Priming

Structural priming investigates how recent exposure to a specific sentence structure influences the subsequent production or processing of similar structures, offering insights into the nature of grammatical representations in both humans and artificial language models. Current research focuses on comparing priming effects across different language models (like RNNs and Transformers), exploring the role of factors such as word frequency and lexical relationships, and examining whether these models exhibit human-like priming behaviors, including cross-lingual effects. These studies contribute to a deeper understanding of human language processing mechanisms and the extent to which artificial intelligence systems replicate aspects of human cognition, potentially informing the development of more human-like language technologies.