Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs¶
Arxiv Link - 2024-04-01 06:01:17
Abstract¶
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.
Socials¶
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| 🚀 Exciting advancements in the world of AI and Large Language Models (LLMs)! 🌐 The rise of LLMs has revolutionized AI capabilities, but the issue of factuality remains a crucial concern. How can we ensure that LLMs provide accurate responses? 🤖 Introducing GraphEval - a novel approach to evaluating LLM performance using a vast test dataset sourced from a knowledge graph with over 10 million facts. 📊 By leveraging a judge model, GraphEval estimates the correctness of LLM outputs, streamlining evaluation processes and reducing costs significantly. Our experiments have shown that the judge model's factuality assessment closely aligns with the accuracy of LLM-generated responses, offering valuable insights into performance metrics and paving the way for future enhancements in ensuring factual integrity. 📈 Curious to learn more? Dive into the details and explore the code at: https://github.com/xz-liu/GraphEval 📝 Read the full paper here: http://arxiv.org/abs/2404.00942v1 📑 #AI #LLMs #GraphEval #MachineLearning #ArtificialIntelligence #TechInnovation #ResearchPaper #GitHub Let's continue pushing the boundaries of AI together! 💡🔍 |
🌟 Exciting research on evaluating Large Language Models (LLMs) using GraphEval for factuality assessment without costly human efforts! Find out how this approach enhances LLM performance and reduces evaluation costs. Read the paper at: http://arxiv.org/abs/2404.00942v1 #AI #NLP #LLM #GraphEval 🤖📊 |