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 for enhanced QA with
knowledge-augmented LLMs. We optimize an open-source LLM as a fact summarizer
through distillation and preference alignment. Our extensive experiments show
that EFSum improves LLM's zero-shot QA performance, and it is possible to
ensure both the helpfulness and faithfulness of the summary.

通过使用知识图谱增强大型语言模型的问答性能，我们提出了 EFSum，一种以证据为中心的事实摘要框架，它通过蒸馏和偏好对齐来优化语言模型作为事实摘要器，从而改进了零 - shot 问答性能，且可以确保摘要的有用性和忠实性。