@inproceedings{amballa-etal-2026-voyager,
    title = "{VOYAGER}: A Training Free Approach for Generating Diverse Datasets using {LLM}s",
    author = "Amballa, Avinash  and
      Saidutta, Yashas Malur  and
      Lin, Chi-Heng  and
      Kulkarni, Vivek  and
      Chappidi, Srinivas",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.784/",
    pages = "17224--17245",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that such generated data lacks diversity. In this paper, we propose VOYAGER, a novel principled approach to generate diverse datasets. Our approach is iterative and directly optimizes a mathematical quantity that optimizes the diversity of the dataset using the machinery of determinantal point processes. Furthermore, our approach is training-free, applicable to closed-source models, and scalable. In addition to providing theoretical justification for the working of our method, we also demonstrate through comprehensive experiments that VOYAGER improves diversity by $\mathbf{1.5}$-$\mathbf{3}$ times compared to popular baseline approaches."
}