@inproceedings{chheda-etal-2025-iterative,
    title = "Iterative Critique-Driven Simplification: Targeted Enhancement of Complex Definitions with Small Language Models",
    author = "Chheda, Veer  and
      Sankhe, Avantika  and
      Ghaisas, Aaditya Uday",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.64/",
    pages = "1081--1096",
    ISBN = "979-8-89176-303-6",
    abstract = "Difficult and unfamiliar concepts often hinder comprehension for lay audiences, especially in technical and educational domains. This motivates the usage of large language models (LLMs) for the process of text simplification (TS). In this work, we propose an iterative refinement framework that aims to simplify definitions by carefully handling complex terminology and domain-specific expressions. The obtained definition is reprocessed based on the critique, making refinements in successive iterations. We emphasize the use of small language models (SLMs) due to their faster response times and cost-efficient deployment. Human evaluations of the definitions produced at each refinement stage indicate consistent improvements in our specified evaluation criteria. We evaluate both LLM-as-a-judge score and human assessments along with automated metrics like BERTScore, BLEU-4, which provided supporting evidence for the effectiveness of our approach. Our work highlights the use of LLMs mimicking human-like feedback system in a TS task catering to a reader{'}s specific cognitive needs. Thus, we find that an iterative, critique-driven method can be an effective strategy for the simplification of dense or technical texts, particularly in domains where jargon impedes understanding."
}