Linguistic Ambiguity

Linguistic ambiguity, the presence of multiple possible meanings in a sentence or phrase, poses a significant challenge for natural language processing (NLP) systems. Current research focuses on how large language models (LLMs), such as GPT and similar transformer-based architectures, handle various types of ambiguity (semantic, syntactic, lexical, scope), often comparing model performance to human interpretation through carefully designed datasets. This work is crucial for improving the accuracy and reliability of NLP applications, ranging from contract analysis and question answering to more robust human-computer interaction and multi-robot collaboration. Addressing ambiguity is key to unlocking the full potential of LLMs and ensuring their responsible deployment.