@inproceedings{li-etal-2026-instruction,
    title = "Instruction Data Selection via Answer Divergence",
    author = "Li, Bo  and
      Wang, Mingda  and
      Zhang, Shikun  and
      Ye, Wei",
    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.214/",
    pages = "4684--4702",
    ISBN = "979-8-89176-390-6",
    abstract = "Instruction tuning relies on large instruction{--}response corpora whose quality and composition strongly affect downstream performance. We propose \textbf{A}nswer \textbf{D}ivergence-\textbf{G}uided Selection \textbf{(ADG)}, which selects instruction data based on the geometric structure of multi-sample outputs. ADG draws several high-temperature generations per instruction, maps responses into an embedding space, and computes an output divergence score that jointly encodes dispersion magnitude and shape anisotropy. High scores correspond to instructions whose answers are both far apart and multi-modal, rather than clustered paraphrases along a single direction. Across two backbones and three public instruction pools, fine-tuning on only 10K ADG-selected examples consistently outperforms strong selectors on six benchmarks spanning reasoning, knowledge, and coding. Analyses further show that both dispersion magnitude and shape anisotropy are necessary, supporting answer divergence as a practical signal for instruction data selection. Code and appendix are included in the supplementary materials."
}