Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering
This work addresses a bottleneck in knowledge-augmented QA for LLM users, offering an incremental improvement over existing verbalization methods.
The paper tackles the challenge of structured knowledge graph verbalization for knowledge-augmented zero-shot question answering by proposing EFSum, an evidence-focused fact summarization framework, which improves LLM performance with enhanced helpfulness and faithfulness in summaries.
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.