@inproceedings{hadifar-etal-2025-regraph,
    title = "{R}e{G}raph: Learning to Reformulate Graph Encodings with Large Language Models",
    author = "Hadifar, Amir  and
      Ochs, Christopher  and
      Van Ewijk, Arjan",
    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.ijcnlp-short.32/",
    pages = "386--394",
    ISBN = "979-8-89176-299-2",
    abstract = "Large language models can rephrase and restructure natural language effectively, but their potential for reformulating graph encodings remains underexplored despite the significant impact of encoding choices on performance.In this work, we introduce ReGraph, a reinforcement learning-based approach that guides language models to reformulate graph encodings for improved task alignment.We demonstrate that reformulating graph encodings enhances reasoning and yields consistent performance gains on graph question answering tasks.Our results show that language models often prefer specific graph encodings, even if they are suboptimal for the task at hand."
}