Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?

Miriam Anschütz; Edoardo Mosca; Georg Groh

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arXiv · 2404.06838

Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?

Miriam Anschütz·Edoardo Mosca·Georg Groh

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Abstract

Text simplification seeks to improve readability while retaining the original content and meaning. Our study investigates whether pre-trained classifiers also maintain such coherence by comparing their predictions on both original and simplified inputs. We conduct experiments using 11 pre-trained models, including BERT and OpenAI's GPT 3.5, across six datasets spanning three languages. Additionally, we conduct a detailed analysis of the correlation between prediction change rates and simplification types/strengths. Our findings reveal alarming inconsistencies across all languages and models. If not promptly addressed, simplified inputs can be easily exploited to craft zero-iteration model-agnostic adversarial attacks with success rates of up to 50%

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Miriam Anschütz, Edoardo Mosca, Georg Groh. 2024-04-10. Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?. https://arxiv.org/abs/2404.06838

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