@inproceedings{singh-etal-2025-empirical,
    title = "An empirical study of validating synthetic data for formula generation",
    author = "Singh, Usneek  and
      Cambronero, Jos{\'e}  and
      Gulwani, Sumit  and
      Kanade, Aditya  and
      Khatry, Anirudh  and
      Le, Vu  and
      Singh, Mukul  and
      Verbruggen, Gust",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.findings-naacl.391/",
    pages = "7047--7054",
    ISBN = "979-8-89176-195-7",
    abstract = "Large language models (LLMs) can be leveraged to help write formulas in spreadsheets, but formula data resources are scarce, impacting both the base performance of pre-trained models and limiting the ability to fine-tune them. Given a corpus of formulas, we can use another model to generate synthetic natural language utterances for fine-tuning. However, it is important to validate whether the natural language (NL) generated by the LLM is accurate for it to be beneficial for fine-tuning. In this paper, we provide empirical results on the impact of validating these synthetic training examples with surrogate objectives that evaluate the accuracy of the synthetic annotations. We demonstrate that validation improves performance over raw data across four models (2 open and 2 closed weight). Interestingly, we show that although validation tends to prune more challenging examples, it increases the complexity of problems that models can solve after being fine-tuned on validated data."
}