Ambiguous Question

Ambiguous questions, those with multiple valid interpretations, pose a significant challenge for question answering (QA) systems. Current research focuses on developing methods to detect and resolve this ambiguity, often employing retrieval-augmented generation (RAG) frameworks, instruction-tuning, and selective decoding strategies within various model architectures, including large language models (LLMs). These efforts aim to improve the accuracy and robustness of QA systems by generating comprehensive answers that address all plausible interpretations or by strategically clarifying the user's intent through clarifying questions. The resulting advancements have implications for improving human-computer interaction and enhancing the reliability of information retrieval across diverse applications.