Manish, Sanwal (2024) Beyond Traditional Benchmarks Leveraging Contrast Sets for Robust LLM Evaluation. International Journal of Informatics and Data Science Research, 1 (9). pp. 66-71. ISSN 2997-3961

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Abstract

The evaluation of AI model robustness is a critical aspect of ensuring the reliability and effectiveness of large language models (LLMs). While traditional evaluation methods often focus on performance metrics like accuracy and fluency, these approaches fail to capture a model’s ability to handle edge cases, ambiguous inputs, or outlier scenarios. Contrast sets, which involve the use of carefully curated input pairs with subtle differences, provide a powerful tool to address this limitation. By testing an LLM on these contrast sets, researchers can gain deeper insights into how well the model generalizes across diverse situations, revealing its weaknesses and vulnerabilities that might otherwise go unnoticed. Contrast sets work by highlighting nuanced differences in input data that challenge a model’s understanding and decision-making processes. This approach enables the detection of hidden biases, inconsistencies, and flaws that may impact the model’s real-world application. Additionally, contrast sets can be tailored to target specific aspects of model performance, such as reasoning ability, knowledge representation, and contextual comprehension. This focused testing offers more fine-grained analysis compared to broad benchmarks. Incorporating contrast sets into LLM benchmarking not only enhances our understanding of model robustness but also promotes fairness and accountability in AI systems. As AI continues to play an increasing role in decision-making processes, it is essential to develop tools that ensure these systems are both reliable and trustworthy. Contrast sets present a promising avenue for improving the robustness evaluation of LLMs, providing valuable insights that drive the development of more reliable, transparent, and equitable AI models. Through the strategic application of contrast sets, we can move towards a more comprehensive and effective approach to AI model evaluation.

Item Type:	Article
Subjects:	L Education > L Education (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	10 Jun 2025 05:35
Last Modified:	10 Jun 2025 05:35
URI:	http://eprints.umsida.ac.id/id/eprint/16194

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