@inproceedings{moon-etal-2025-limacost,
    title = "{L}ima{C}ost: Data Valuation for Instruction Tuning of Large Language Models",
    author = "Moon, Hyeonseok  and
      Seo, Jaehyung  and
      Koo, Seonmin  and
      Kim, Jinsung  and
      Ham, Young-kyoung  and
      Moon, Jiwon  and
      Lim, Heuiseok",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.688/",
    doi = "10.18653/v1/2025.findings-emnlp.688",
    pages = "12841--12854",
    ISBN = "979-8-89176-335-7",
    abstract = "Instruction tuning (IT) is an effective approach for aligning large language models (LLMs) with human intentions. There is ongoing discourse regarding the data quality for IT. As an effort to find the robust criteria of data quality for IT, we introduce LimaCost, a data quality measure that exhibits a strong correlation with model performance. LimaCost utilizes LIMA dataset, which effectiveness in IT has already been validated by several previous works. LimaCost then estimates the value of a given data by estimating how many LIMA data points might be needed to approximate its gradient. Our experiments reveal that LimaCost enables effective data selection that derive high alignment performance. We demonstrate that selecting data based on high LimaCost proves to be more effective than existing data selection strategies."
}