The content introduces EFSUM, a novel framework for enhancing zero-shot Question Answering (QA) by summarizing facts with high evidence density and clarity. The approach optimizes LLMs as fact summarizers through distillation and preference alignment, significantly improving QA performance. Various experiments validate the effectiveness of EFSUM in generating helpful and faithful summaries based on relevant facts and questions.
Recent studies have explored utilizing Knowledge Graphs (KGs) to enhance Large Language Models (LLMs) in QA tasks. Existing methods face challenges in structured KG verbalization, leading to reduced evidence density and clarity. To address these issues, the author proposes EFSUM as a solution for enhanced QA performance with knowledge-augmented LLMs.
EFSUM focuses on transforming sets of facts into coherent summaries while emphasizing evidence and filtering out noise. By optimizing LLMs through distillation and preference alignment, EFSUM significantly improves zero-shot QA performance by ensuring both helpfulness and faithfulness of the generated summaries.
toiselle kielelle
lähdeaineistosta
arxiv.org
Tärkeimmät oivallukset
by Sungho Ko,Hy... klo arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.02966.pdfSyvällisempiä Kysymyksiä