Multiple Plausible Answer

Multiple plausible answers pose a significant challenge in question answering (QA) systems, demanding models capable of identifying and presenting diverse, valid responses, along with justifications and source citations. Current research focuses on developing models that handle ambiguity through techniques like iterative prompting, retrieval-augmented generation, and the use of determinantal point processes for diverse answer retrieval, often incorporating large language models (LLMs) as core components. Addressing this challenge is crucial for building more robust and trustworthy QA systems, improving information access and facilitating more nuanced understanding in various domains, from scientific research to conversational AI.