Ambiguous Natural Language

Ambiguous natural language, characterized by multiple possible interpretations of a sentence or phrase, poses a significant challenge for natural language processing (NLP). Current research focuses on developing models, including large language models (LLMs) and visual-language models, that can effectively resolve this ambiguity using contextual cues, such as surrounding text or accompanying images, and by explicitly training models to recognize and manage ambiguity. This work is crucial for improving the reliability and robustness of NLP systems in applications like question answering, machine translation, and code generation, where accurate interpretation of potentially ambiguous user input is paramount. The development of new benchmarks and datasets specifically designed to evaluate ambiguity resolution capabilities is also a key area of ongoing effort.