Summary of Evidence-focused Fact Summarization For Knowledge-augmented Zero-shot Question Answering, by Sungho Ko et al.
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering
by Sungho Ko, Hyunjin Cho, Hyungjoo Chae, Jinyoung Yeo, Dongha Lee
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study proposes an Evidence-focused Fact Summarization framework (EFSum) to enhance Question Answering (QA) performance of Large Language Models (LLMs) utilizing Knowledge Graphs (KGs). EFSum optimizes an open-source LLM as a fact summarizer through distillation and preference alignment. The proposed method addresses existing issues in structured KG verbalization, such as reduced evidence density and clarity, by emphasizing crucial evidence. Experimental results demonstrate that EFSum improves LLM’s zero-shot QA performance while ensuring the helpfulness and faithfulness of the summary. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EFSum is a new way to help computers answer questions better using large amounts of information. It takes a big database called a Knowledge Graph and converts it into a shorter, easier-to-understand format that machines can use to find answers. This helps the machine learn and get smarter at answering questions without needing extra training data. |
Keywords
* Artificial intelligence * Alignment * Distillation * Knowledge graph * Question answering * Summarization * Zero shot
