Samarth Oza

Tree-of-Quote Prompting Improves Factuality and Attribution in Multi-Hop and Medical Reasoning

Large language models (LLMs) can produce fluent but factually incorrect outputs and often have limited ability to attribute their claims to source material. This undermines their reliability, particularly in multi-hop and high-stakes domains such as medicine. We propose Tree-of-Quote (ToQ) prompting