Enhanced Spreadsheet Computing with Finite-Domain Constraint Satisfaction

• URL: http://arxiv.org/abs/2203.16346v1
• Date: Tue, 22 Feb 2022 17:58:08 GMT
• Title: Enhanced Spreadsheet Computing with Finite-Domain Constraint Satisfaction
• Authors: Ezana N. Beyenne and Hai-Feng Guo
• Abstract summary: We present an enhanced spreadsheet system where finite-domain constraint solving is well supported in a visual environment. A spreadsheet-specific constraint language is constructed for general users to specify constraints among data cells. The new spreadsheet system significantly simplifies the development of many constraint-based applications.
• Score: 1.6244541005112747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The spreadsheet application is among the most widely used computing tools in modern society. It provides excellent usability and usefulness, and it easily enables a non-programmer to perform programming-like tasks in a visual tabular "pen and paper" approach. However, spreadsheets are mostly limited to bookkeeping-like applications due to their mono-directional data flow. This paper shows how the spreadsheet computing paradigm is extended to break this limitation for solving constraint satisfaction problems. We present an enhanced spreadsheet system where finite-domain constraint solving is well supported in a visual environment. Furthermore, a spreadsheet-specific constraint language is constructed for general users to specify constraints among data cells in a declarative and scalable way. The new spreadsheet system significantly simplifies the development of many constraint-based applications using a visual tabular interface. Examples are given to illustrate the usability and usefulness of the extended spreadsheet paradigm. KEYWORDS: Spreadsheet computing, Finite-domain constraint satisfaction, Constraint logic programming

Related papers

• LaTable: Towards Large Tabular Models [63.995130144110156]
  Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
  arXiv  Detail & Related papers  (2024-06-25T16:03:50Z)
• SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation [34.8332394229927]
  SpreadsheetBench is designed to immerse current large language models (LLMs) in the actual workflow of spreadsheet users. Unlike existing benchmarks that rely on synthesized queries and simplified spreadsheet files, SpreadsheetBench is built from 912 real questions gathered from online Excel forums. Our comprehensive evaluation of various LLMs under both single-round and multi-round inference settings reveals a substantial gap between the state-of-the-art (SOTA) models and human performance.
  arXiv  Detail & Related papers  (2024-06-21T09:06:45Z)
• Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations [36.2969566996675]
  We develop an Auto-Formula system that can accurately predict formulas that users want to author in a target spreadsheet cell. We use contrastive-learning techniques inspired by "similar-face recognition" from compute vision.
  arXiv  Detail & Related papers  (2024-04-19T03:28:18Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models [60.171444066848856]
  We propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements. We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline. Our SheetCopilot correctly completes 44.3% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin.
  arXiv  Detail & Related papers  (2023-05-30T17:59:30Z)
• STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables [64.0903766169603]
  We propose a framework for few-shot semi-supervised learning, coined Self-generated Tasks from UNlabeled Tables (STUNT) Our key idea is to self-generate diverse few-shot tasks by treating randomly chosen columns as a target label. We then employ a meta-learning scheme to learn generalizable knowledge with the constructed tasks.
  arXiv  Detail & Related papers  (2023-03-02T02:37:54Z)
• Spreadsheet computing with Finite Domain Constraint Enhancements [0.0]
  We present a framework seamlessly incorporating a finite constraint solver with the spreadsheet computing paradigm. The framework provides an interface for constraint solving and further enhances the spreadsheet computing paradigm.
  arXiv  Detail & Related papers  (2022-02-22T17:50:48Z)
• SpreadsheetCoder: Formula Prediction from Semi-structured Context [70.41579328458116]
  We propose a BERT-based model architecture to represent the tabular context in both row-based and column-based formats. We train our model on a large dataset of spreadsheets, and demonstrate that SpreadsheetCoder achieves top-1 prediction accuracy of 42.51%. Compared to the rule-based system, SpreadsheetCoder 82% assists more users in composing formulas on Google Sheets.
  arXiv  Detail & Related papers  (2021-06-26T11:26:27Z)
• An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
  We present a general framework for mining constraints from data. In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem. We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
  arXiv  Detail & Related papers  (2020-06-18T20:09:53Z)
• A Structured Approach to the development of Solutions in Excel [0.0]
  This paper considers the use of controversial or lesser-used techniques to create a coherent solution strategy. The problem is solved by a sequence of formulas resembling the steps of a programmed language.
  arXiv  Detail & Related papers  (2017-04-04T18:22:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.