Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering

arXiv:2403.02966v1 Announce Type: cross
Abstract: Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization remains challengin. Existing methods, such as triple-form or free-form textual conversion of triple-form facts, encounter several issues. These include reduced evidence density due to duplicated entities or relationships, and reduced evidence clarity due to an inability to emphasize crucial evidence. To address these issues, we propose EFSum, an Evidence-focused Fact Summarization framework …

abstract arxiv conversion cs.ai cs.cl cs.lg evidence facts form free graphs knowledge knowledge graphs language language models large language large language models llms performance question question answering studies summarization textual type zero-shot