@inproceedings{saha-etal-2026-reference,
    title = "Reference-Free Schema Generation for Literature Review Tables via Multi-Faceted Rewards",
    author = "Saha, Sinjoy  and
      Saha, Suman  and
      Farooque, Mahfuza  and
      Yin, Wenpeng",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.111/",
    pages = "1253--1261",
    ISBN = "979-8-89176-393-7",
    abstract = "To accelerate scientific knowledge acquisition, LLMs are increasingly used to synthesize multiple papers into structured tables by inferring schemas and values. While value generation within a fixed schema can often be reduced to extractive question answering, the schema generation problem, determining which dimensions to compare a set of documents, lacks a formal mapping to standard NLP tasks. In this work, we formulate schema generation as a reinforcement learning problem and investigate whether these dimensions can be induced without access to gold-standard schemas. We design a multi-faceted reward framework capturing schema coverage, non-redundancy, relevance, and format, and train a small language model on a literature review dataset. Our approach yields consistent improvements over the untuned base model across intrinsic, reference-based, and LLM-judge metrics, and remains competitive with supervised fine-tuned models at 5$\times$ the parameter count on structural and diversity dimensions. All code, results and prompts are available in the GitHub repository: \url{https://github.com/sinjoysaha/rl-schema-generation}"
}