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Computer Science > Programming Languages

arXiv:2301.13779 (cs)
[Submitted on 31 Jan 2023 (v1), last revised 19 Dec 2023 (this
version, v2)]

Title:FLAME: A small language model for spreadsheet formulas

Authors:Harshit Joshi, Abishai Ebenezer, Jose Cambronero, Sumit
Gulwani, Aditya Kanade, Vu Le, Ivan Radicek, Gust Verbruggen
View a PDF of the paper titled FLAME: A small language model for
spreadsheet formulas, by Harshit Joshi and 7 other authors
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    Abstract:Spreadsheets are a vital tool for end-user data
    management. Using large language models for formula authoring
    assistance in these environments can be difficult, as these
    models are expensive to train and challenging to deploy due to
    their size (up to billions of parameters). We present FLAME, a
    transformer-based model trained exclusively on Excel formulas
    that leverages domain insights to achieve competitive performance
    while being substantially smaller (60M parameters) and training
    on two orders of magnitude less data. We curate a training
    dataset using sketch deduplication, introduce an Excel-specific
    formula tokenizer, and use domain-specific versions of masked
    span prediction and noisy auto-encoding as pre-training
    objectives. We evaluate FLAME on formula repair, formula
    completion, and similarity-based formula retrieval. FLAME can
    outperform much larger models, such as the Davinci (175B) and
    Cushman (12B) variants of Codex and CodeT5 (220M), in 10 of 14
    evaluation settings for the repair and completion tasks. For
    formula retrieval, FLAME outperforms CodeT5, CodeBERT, and
    GraphCodeBERT.

Comments: Accepted to AAAI 2024
Subjects: Programming Languages (cs.PL); Artificial Intelligence
          (cs.AI); Software Engineering (cs.SE)
Cite as:  arXiv:2301.13779 [cs.PL]
          (or arXiv:2301.13779v2 [cs.PL] for this version)
          https://doi.org/10.48550/arXiv.2301.13779
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Harshit Joshi [view email]
[v1] Tue, 31 Jan 2023 17:29:43 UTC (230 KB)
[v2] Tue, 19 Dec 2023 22:56:39 UTC (422 KB)
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