@inproceedings{zhou-etal-2025-p2,
    title = "p{\texttwosuperior}-{TQA}: A Process-based Preference Learning Framework for Self-Improving Table Question Answering Models",
    author = "Zhou, Wei  and
      Mesgar, Mohsen  and
      Adel, Heike  and
      Friedrich, Annemarie",
    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.20/",
    pages = "217--231",
    ISBN = "979-8-89176-299-2",
    abstract = "Table question answering (TQA) focuses on answering questions based on tabular data. Developing TQA systems targets effective interaction with tabular data for tasks such as cell retrieval and data analysis. While recent work has leveraged fine-tuning to improve TQA systems, existing approaches often under-utilize available data and neglect the potential of post-training for further gains. In this work, we introduce p{\texttwosuperior}-TQA, a process-based preference learning framework for TQA post-training. p{\texttwosuperior}-TQA automatically constructs process-based preference data via a table-specific pipeline, eliminating the need for manual or costly data collection. It then optimizes models through contrastive learning on the collected data. Experiments show that p{\texttwosuperior}-TQA effectively improves TQA models by up to 5{\%} on in-domain datasets and 2.4{\%} on out-of-domain datasets with only 8,000 training instances. Furthermore, models enhanced with p{\texttwosuperior}-TQA achieve competitive results against larger, more complex state-of-the-art TQA systems, while maintaining up to five times higher efficiency."
}