A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios

• URL: http://arxiv.org/abs/2408.01963v4
• Date: Mon, 4 Nov 2024 13:32:40 GMT
• Title: A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios
• Authors: Samuel Ackerman, Ella Rabinovich, Eitan Farchi, Ateret Anaby-Tavor,
• Abstract summary: We evaluate the robustness of several large language models on multiple datasets. Benchmark datasets are constructed by introducing naturally-preserving, non-malicious perturbations.
• Score: 5.617202699068449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We evaluate the robustness of several large language models on multiple datasets. Robustness here refers to the relative insensitivity of the model's answers to meaning-preserving variants of their input. Benchmark datasets are constructed by introducing naturally-occurring, non-malicious perturbations, or by generating semantically equivalent paraphrases of input questions or statements. We further propose a novel metric for assessing a model robustness, and demonstrate its benefits in the non-adversarial scenario by empirical evaluation of several models on the created datasets.

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