Summary of How Reliable Are Llms As Knowledge Bases? Re-thinking Facutality and Consistency, by Danna Zheng et al.
How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency
by Danna Zheng, Mirella Lapata, Jeff Z. Pan
First submitted to arxiv on: 18 Jul 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 reevaluation of Large Language Models (LLMs) is proposed as knowledge bases (KBs), moving beyond traditional knowledge retention metrics. Two essential factors are highlighted: factuality, ensuring accurate responses to both seen and unseen knowledge, and consistency, maintaining stable answers about the same knowledge. A new dataset, UnseenQA, is introduced to assess LLM performance on unseen knowledge, accompanied by novel criteria and metrics for quantifying factuality and consistency, leading to a final reliability score. Experimental results on 26 LLMs reveal challenges in their use as KBs, emphasizing the need for more comprehensive evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are like super smart computers that can understand and generate human-like text. Right now, people are trying to figure out if these models can be used to store and share information, kind of like a dictionary or encyclopedia. But current methods only check how well the model remembers what it’s already learned, without considering other important things. This paper wants to change that by looking at two key things: does the model give accurate answers, even when faced with new information, and is it consistent in its responses about the same topic? To test this, the authors created a special dataset called UnseenQA and came up with new ways to measure how well the models do. When they tried it out on 26 different LLMs, they found that there are some big challenges to using these models as information storage systems. |
