FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation

• URL: http://arxiv.org/abs/2504.17311v1
• Date: Thu, 24 Apr 2025 07:12:37 GMT
• Title: FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation
• Authors: Yulia Otmakhova, Hung Thinh Truong, Rahmad Mahendra, Zenan Zhai, Rongxin Zhu, Daniel Beck, Jey Han Lau,
• Abstract summary: FLUKE is a task-agnostic framework for assessing model robustness through systematic minimal variations of test data.<n>We demonstrate FLUKE's utility by evaluating both fine-tuned models and large language models (LLMs) across four diverse NLP tasks.
• Score: 21.850854237079595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present FLUKE (Framework for LingUistically-driven and tasK-agnostic robustness Evaluation), a task-agnostic framework for assessing model robustness through systematic minimal variations of test data. FLUKE introduces controlled variations across linguistic levels - from orthography to dialect and style varieties - and leverages large language models (LLMs) with human validation to generate modifications. We demonstrate FLUKE's utility by evaluating both fine-tuned models and LLMs across four diverse NLP tasks, and reveal that (1) the impact of linguistic variations is highly task-dependent, with some tests being critical for certain tasks but irrelevant for others; (2) while LLMs have better overall robustness compared to fine-tuned models, they still exhibit significant brittleness to certain linguistic variations; (3) all models show substantial vulnerability to negation modifications across most tasks. These findings highlight the importance of systematic robustness testing for understanding model behaviors.

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