Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
This addresses the critical issue of factual accuracy in LLMs for AI applications, though it is incremental as it builds on existing evaluation methods with a new dataset and judge model.
The paper tackles the problem of evaluating factuality in Large Language Models by proposing GraphEval, which uses a knowledge graph with over 10 million facts as a test dataset and a judge model to assess correctness, reducing evaluation costs while aligning closely with actual LLM performance.
The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern for LLMs, as they may generate factually incorrect responses. In this paper, we propose GraphEval to evaluate an LLM's performance using a substantially large test dataset. Specifically, the test dataset is retrieved from a large knowledge graph with more than 10 million facts without expensive human efforts. Unlike conventional methods that evaluate LLMs based on generated responses, GraphEval streamlines the evaluation process by creating a judge model to estimate the correctness of the answers given by the LLM. Our experiments demonstrate that the judge model's factuality assessment aligns closely with the correctness of the LLM's generated outputs, while also substantially reducing evaluation costs. Besides, our findings offer valuable insights into LLM performance across different metrics and highlight the potential for future improvements in ensuring the factual integrity of LLM outputs. The code is publicly available at https://github.com/xz-liu/GraphEval.