# SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models

arXiv: https://arxiv.org/abs/2403.03636 · HTML: https://mlsanity.com/paper/2403.03636/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 30 of its 47 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)
- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [StructGPT: A General Framework for Large Language Model to Reason over Structured Data](https://mlsanity.com/paper/2305.09645/) (2023)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [OS-Copilot: Towards Generalist Computer Agents with Self-Improvement](https://mlsanity.com/paper/2402.07456/) (2024)
- [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023)
- [FeTaQA: Free-form Table Question Answering](https://mlsanity.com/paper/2104.00369/) (2021)
- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [Large Language Models are few(1)-shot Table Reasoners](https://mlsanity.com/paper/2210.06710/) (2022)
- [AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems](https://mlsanity.com/paper/2310.09233/) (2023)
- [Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow](https://mlsanity.com/paper/2306.07209/) (2023)
- [Table-GPT: Table-tuned GPT for Diverse Table Tasks](https://mlsanity.com/paper/2310.09263/) (2023)
- [TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT](https://mlsanity.com/paper/2307.08674/) (2023)
- [SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models](https://mlsanity.com/paper/2305.19308/) (2023)
- [SpreadsheetCoder: Formula Prediction from Semi-structured Context](https://mlsanity.com/paper/2106.15339/) (2021)
- [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://mlsanity.com/paper/2207.03637/) (2022)
- [InstructExcel: A Benchmark for Natural Language Instruction in Excel](https://mlsanity.com/paper/2310.14495/) (2023)
- [Parameter-Efficient Abstractive Question Answering over Tables or Text](https://mlsanity.com/paper/2204.03357/) (2022)
- [Enhancing Robotic Manipulation with AI Feedback from Multimodal Large Language Models](https://mlsanity.com/paper/2402.14245/) (2024)
- [FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language](https://mlsanity.com/paper/2310.17306/) (2023)

## Built on by (most cited first, 17 of 17)

- [Transforming Wearable Data into Personal Health Insights using Large Language Model Agents](https://mlsanity.com/paper/2406.06464/) (2024)
- [SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows](https://mlsanity.com/paper/2606.29955/) (2026)
- [Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows](https://mlsanity.com/paper/2512.13168/) (2025)
- [FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats](https://mlsanity.com/paper/2408.08841/) (2024)
- [PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks](https://mlsanity.com/paper/2606.31154/) (2026)
- [MBABench: Evaluating LLM Agents on End-to-End Spreadsheet Tasks in Finance](https://mlsanity.com/paper/2605.22664/) (2026)
- [PrepBench: How Far Are We from Natural-Language-Driven Data Preparation?](https://mlsanity.com/paper/2605.08687/) (2026)
- [Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence](https://mlsanity.com/paper/2507.10281/) (2025)
- [BlueFin: Benchmarking LLM Agents on Financial Spreadsheets](https://mlsanity.com/paper/2605.30907/) (2026)
- [EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management](https://mlsanity.com/paper/2606.03841/) (2026)
- [Agentic Evaluation of Copyright Law Compliance](https://mlsanity.com/paper/2607.21799/) (2026)
- [GPG: Generalized Policy Gradient Theorem for Transformer-based Policies](https://mlsanity.com/paper/2512.10365/) (2025)
- [Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning](https://mlsanity.com/paper/2605.22642/) (2026)
- [Plans Work in Mysterious Ways: Evaluating a Plan Mode for Spreadsheet Agents](https://mlsanity.com/paper/2607.23670/) (2026)
- [CITBench: A Comprehensive Benchmark for Interactive Tabular Data Processing with LLMs](https://mlsanity.com/paper/2608.00018/) (2026)
- [Rethinking Multi-Agent Collaboration: When More Is Less](https://mlsanity.com/paper/2609.19759/) (2026)
- [CRAFT: An Agentic Spreadsheet Form Filling System with Template Awareness](https://mlsanity.com/paper/2610.04437/) (2026)

## Abstract (arXiv)

Spreadsheets are ubiquitous across the World Wide Web, playing a critical role in enhancing work efficiency across various domains. Large language model (LLM) has been recently attempted for automatic spreadsheet manipulation but has not yet been investigated in complicated and realistic tasks where reasoning challenges exist (e.g., long horizon manipulation with multi-step reasoning and ambiguous requirements). To bridge the gap with the real-world requirements, we introduce SheetRM, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges. To mitigate the above challenges, we further propose SheetAgent, a novel autonomous agent that utilizes the power of LLMs. SheetAgent consists of three collaborative modules: Planner, Informer, and Retriever, achieving both advanced reasoning and accurate manipulation over spreadsheets without human interaction through iterative task reasoning and reflection. Extensive experiments demonstrate that SheetAgent delivers 20--40% pass rate improvements on multiple benchmarks over baselines, achieving enhanced precision in spreadsheet manipulation and demonstrating superior table reasoning abilities. More details and visualizations are available at the project website: https://sheetagent.github.io/. The datasets and source code are available at https://anonymous.4open.science/r/SheetAgent.