Summary of Evaluating the Factuality Of Large Language Models Using Large-scale Knowledge Graphs, by Xiaoze Liu et al.
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
by Xiaoze Liu, Feijie Wu, Tianyang Xu, Zhuo Chen, Yichi Zhang, Xiaoqian Wang, Jing Gao
First submitted to arxiv on: 1 Apr 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 The proposed GraphEval method evaluates the performance of Large Language Models (LLMs) using a large test dataset retrieved from a knowledge graph, without requiring expensive human effort. Unlike conventional methods, GraphEval uses a judge model to estimate the correctness of an LLM’s generated outputs. This approach demonstrates high alignment with the correctness of LLM outputs while significantly reducing evaluation costs. The findings also provide valuable insights into LLM performance across different metrics and highlight potential future improvements for ensuring the factual integrity of LLM outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are changing the way we use AI, making it easier to learn and improve machines. But sometimes these models can give wrong answers. This paper is about a new way to check if an LLM’s answer is correct or not. It uses a big database of facts to test the model without needing lots of human help. The results show that this method works well, and it could be used to make sure AI systems are giving good information. |
Keywords
* Artificial intelligence * Alignment * Knowledge graph
