Seeing Through the Fog: A Cost-Effectiveness Analysis of Hallucination Detection Systems

• URL: http://arxiv.org/abs/2411.05270v1
• Date: Fri, 08 Nov 2024 02:06:41 GMT
• Title: Seeing Through the Fog: A Cost-Effectiveness Analysis of Hallucination Detection Systems
• Authors: Alexander Thomas, Seth Rosen, Vishnu Vettrivel,
• Abstract summary: We evaluate hallucination detection systems using the diagnostic odds ratio (DOR) and cost-effectiveness metrics. Our results indicate that although advanced models can perform better they come at a much higher cost.
• Score: 45.3392300968787
• License:
• Abstract: This paper presents a comparative analysis of hallucination detection systems for AI, focusing on automatic summarization and question answering tasks for Large Language Models (LLMs). We evaluate different hallucination detection systems using the diagnostic odds ratio (DOR) and cost-effectiveness metrics. Our results indicate that although advanced models can perform better they come at a much higher cost. We also demonstrate how an ideal hallucination detection system needs to maintain performance across different model sizes. Our findings highlight the importance of choosing a detection system aligned with specific application needs and resource constraints. Future research will explore hybrid systems and automated identification of underperforming components to enhance AI reliability and efficiency in detecting and mitigating hallucinations.

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