ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated
  Factual Question Answering
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.
689e506ea4caabb4320e5c9e
Shubhra Ghosh
689e506ea4caabb4320e5c9f
Abhilekh Borah
689e506ea4caabb4320e5ca0
Aditya Kumar Guru
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2025-09-01T07:54:39.606Z
661a5a093557013b6374405b
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Aditya Guru
user
Adignite
689e506ea4caabb4320e5ca1
Kripabandhu Ghosh
ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated
  Factual Question Answering
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.
ObfusQA, a novel framework with multi-tiered obfuscation levels, evaluates the robustness and adaptability of Large Language Models (LLMs) by examining their performance on obfuscated questions.
Large Language Models LLMs ObfusQAte ObfusQA Named-Entity Indirection Distractor Indirection Contextual Overload hallucinated responses