Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations
This addresses the problem of formula authoring for billions of non-expert spreadsheet users, representing an incremental improvement by adapting existing formulas from similar spreadsheets.
The paper tackles the challenge of authoring complex formulas in spreadsheets for non-technical users by developing Auto-Formula, a system that predicts formulas using contrastive learning on similar spreadsheets, achieving effective results in evaluations on over 2K test formulas from real enterprise data.
Spreadsheets are widely recognized as the most popular end-user programming tools, which blend the power of formula-based computation, with an intuitive table-based interface. Today, spreadsheets are used by billions of users to manipulate tables, most of whom are neither database experts nor professional programmers. Despite the success of spreadsheets, authoring complex formulas remains challenging, as non-technical users need to look up and understand non-trivial formula syntax. To address this pain point, we leverage the observation that there is often an abundance of similar-looking spreadsheets in the same organization, which not only have similar data, but also share similar computation logic encoded as formulas. We develop an Auto-Formula system that can accurately predict formulas that users want to author in a target spreadsheet cell, by learning and adapting formulas that already exist in similar spreadsheets, using contrastive-learning techniques inspired by "similar-face recognition" from compute vision. Extensive evaluations on over 2K test formulas extracted from real enterprise spreadsheets show the effectiveness of Auto-Formula over alternatives. Our benchmark data is available at https://github.com/microsoft/Auto-Formula to facilitate future research.