Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?
<|reference_start|>Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?: 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%<|reference_end|>
@article{anschütz2024simpler,
    title={Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on
  Simplified Corpora?},
    author={Miriam Ansch"utz, Edoardo Mosca, Georg Groh},
    journal={arXiv preprint arXiv:2404.06838},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06838},
    primaryClass={cs.CL}
}