Objective Metrics for Evaluating Large Language Models Using External Data Sources

• URL: http://arxiv.org/abs/2508.08277v1
• Date: Fri, 01 Aug 2025 02:24:19 GMT
• Title: Objective Metrics for Evaluating Large Language Models Using External Data Sources
• Authors: Haoze Du, Richard Li, Edward Gehringer,
• Abstract summary: This paper proposes a framework for leveraging subjective metrics derived from the class textual materials across different semesters.<n>The framework emphasizes automation and transparency in scoring, reducing reliance on human interpretation.<n>This method addresses the limitations of subjective evaluation methods, providing a scalable solution for performance assessment in educational, scientific, and other high-stakes domains.
• Score: 4.574672973076743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluating the performance of Large Language Models (LLMs) is a critical yet challenging task, particularly when aiming to avoid subjective assessments. This paper proposes a framework for leveraging subjective metrics derived from the class textual materials across different semesters to assess LLM outputs across various tasks. By utilizing well-defined benchmarks, factual datasets, and structured evaluation pipelines, the approach ensures consistent, reproducible, and bias-minimized measurements. The framework emphasizes automation and transparency in scoring, reducing reliance on human interpretation while ensuring alignment with real-world applications. This method addresses the limitations of subjective evaluation methods, providing a scalable solution for performance assessment in educational, scientific, and other high-stakes domains.

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