ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated Factual Question Answering

• URL: http://arxiv.org/abs/2508.07321v1
• Date: Sun, 10 Aug 2025 12:27:52 GMT
• Title: ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated Factual Question Answering
• Authors: Shubhra Ghosh, Abhilekh Borah, Aditya Kumar Guru, Kripabandhu Ghosh,
• Abstract summary: Large Language Models (LLMs) have contributed to the development of AI systems capable of factual question-answering.<n>No known study tests the LLMs' robustness when presented with obfuscated versions of questions.<n>We introduce ObfusQA, a framework with multi-tiered obfuscation levels to examine LLM capabilities.
• Score: 3.131352561462676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid proliferation of Large Language Models (LLMs) has significantly contributed to the development of equitable AI systems capable of factual question-answering (QA). However, no known study tests the LLMs' robustness when presented with obfuscated versions of questions. To systematically evaluate these limitations, we propose a novel technique, ObfusQAte and, leveraging the same, introduce ObfusQA, a comprehensive, first of its kind, framework with multi-tiered obfuscation levels designed to examine LLM capabilities across three distinct dimensions: (i) Named-Entity Indirection, (ii) Distractor Indirection, and (iii) Contextual Overload. By capturing these fine-grained distinctions in language, ObfusQA provides a comprehensive benchmark for evaluating LLM robustness and adaptability. Our study observes that LLMs exhibit a tendency to fail or generate hallucinated responses when confronted with these increasingly nuanced variations. To foster research in this direction, we make ObfusQAte publicly available.

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