Summary of Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering, by Yuan Sui et al.
Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering
by Yuan Sui, Yufei He, Zifeng Ding, Bryan Hooi
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel benchmark, OKGQA, is introduced to assess Large Language Models (LLMs) enhanced with Knowledge Graphs (KGs) for open-ended, real-world question answering scenarios. The benchmark aims to evaluate the performance of LLMs in reasoning accuracy and mitigate hallucination problems. Additionally, a variant, OKGQA-P, is proposed to simulate real-world KGs with varying levels of mistakes. This study explores whether integrating KGs can improve trustworthiness in open-ended settings and conduct comparative analyses to shed light on method design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OKGQA is a new benchmark that helps assess how well Large Language Models work when they are connected to Knowledge Graphs. This connection makes the models better at answering questions and reduces mistakes. The benchmark has two parts: one where the model works correctly, and another where the Knowledge Graph has mistakes. This study shows how OKGQA can help make language models more trustworthy and accurate. |
Keywords
» Artificial intelligence » Hallucination » Knowledge graph » Question answering
