T able LLM : Enabling Tabular Data Manipulation by LLM s in Real Office Usage Scenarios (2025)

Abstract: We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to understand reasoning patterns more effectively as well as a cross-way validation strategy, ensuring the quality of the automatically generated data. To evaluate the performance of TableLLM, we have crafted benchmarks tailored to address both document and spreadsheet formats as well as constructed a well-organized evaluation pipeline capable of handling both scenarios. Thorough evaluations underscore the advantages of TableLLM when compared to various existing general-purpose and tabular data-focused LLMs. We have publicly released the model checkpoint, source code, benchmarks, and a web application for user interaction on this anonymized repository.

## introduction large amounts of data are organized in tabular form and widely used in various scenarios. however, working with tabular data can be challenging, as many table-related tasks are laborious, error-prone, and require specialized skills. automating these tasks offers significant benefits to both academic and industrial sectors, attracting considerable interest @xcite . to capture insights from office users, we conduct an extensive user study involving a questionnaire distributed to 507 diverse paiticipants, focusing on table-related tasks. details about the survey are tablerelated tasks (tableqa, table revision, chart, table matching, duplicate data removal, etc.); (b) table formats (excel, word, etc.); (c) table length (small: < 50 rows, large: ≥ 50 rows). previous studies have focused on improving a model's reasoning capabilities for table question answering. moving beyond tableqa, some of these endeavors have also tackled diverse table-related tasks, including fact verification @xcite @xcite , column type annotation @xcite , table matching @xcite , schema augmentation @xcite , and more. however, existing methods for handling tabular data in real-world office scenarios have limitations. some use llms to directly extract answers from internal parameters, which is effective for document-embedded tables but inadequate for long tables and diverse spreadsheet operations. others focus on writing and executing code for spreadsheets but struggle with hybrid queries combining text and tabular data. based on this insight, we present tablellm, specifically designed to handle a wide array of table operations in spreadsheet and document usage scenarios, named tabular data manipulation in real office usage scenarios. to facilitate model training, we introduce a distant supervision method that complements the reasoning process of existing benchmarks, aiding in training llms to understand reasoning patterns more effectively. additionally, we validate the automatically generated training data through a cross-way validation strategy, ensuring data quality. we also provide a theoretical analysis of the effectiveness of crossway validation compared to self-check and sameway validation. utilizing this training data, we fine-tune llama3.1(8b) @xcite , resulting in the development of tablellm. this model adeptly handles document-embedded tabular data through an inner-parameter-driven approach and spreadsheet-embedded tabular data via a codedriven method. a rigorous performance assessment is conducted, involving the collection of primary tableqa test instances from existing benchmarks and the creation of additional table manipulation instances by an annotation team. given the complex evaluation process under the two scenarios, we design a meticulous evaluation method that considers query, update, merge and chart operations. tablellm proves to be on par with the most capable commercial llm gpt-4o in the documentembedded scenario, and even outperforms gpt-4o in the spreadsheet-embedded scenario. in the realm of tabular data processing research, our contributions encompass: (1) addressing a practical problem of tabular data manipulation in real-world office usage scenarios. (2) presenting techniques that extend reasoning processing and in-tegrate a cross-way validation strategy to enhance the quality of distant supervision training data. @xcite delivering a high-quality open-source llm tailored for tabular data manipulation in 8b, thereby enhancing accessibility and fostering collaboration within the community. (4) offering an online application service to facilitate convenient usage. ## related work table question answering. beyond the primary tableqa task, various research endeavors tackle basic table analysis tasks such as fact verification, column type annotation and schema augmentation, typically involving web-extracted tables of relatively short length with textual content. this research has evolved through three main approaches: (1) representation learning: many traditional methods, such as tabert @xcite , tapas @xcite , tablegpt @xcite , tabbie @xcite and tag-qa @xcite , focus on intricate encoder designs with various positional encodings and attention mechanisms, while tapex @xcite and grappa @xcite integrate sql execution as a pre-training task; (2) finetuning llm: researchers leverage llm to train unified models like tablellama @xcite , tat-llm @xcite and tablegpt2 @xcite on multiple table-related benchmarks, with unifiedskg (xie et al., 2022) extending this to structured data tasks; and (3) prompting llm: researchers develop multi-step prompting strategies for gpt series models, employing tools like sql and python, such as dater's sql usage @xcite , structgpt's self-defined interfaces @xcite , and binder's result integration method @xcite . ## problem definition tabular data refers to data organized in table or grid format. on top of it, document-embedded tabular data is tabular data integrated into documents, often in word/pdf files, while spreadsheetembedded tabular data refers to tables within spreadsheets, typically in excel/csv files. ## tablellm the overview design of tablellm is shown in figure 3 , which consists two primary aspects: @xcite distant supervision data construction. the development of distant supervision data involves the integration of both existing benchmark training data and new questions and answers generated from available tabular data. to enhance the training of llms, we suggest expanding the reasoning processes within benchmark data. additionally, to assure the quality of the automatically generated training data, we introduce a cross-way validation strategy which utilizes diverse solution methods for cross-validation. (2) model training. the training process utilizes distinct prompts for documentembedded and spreadsheet-embedded tabular data. ## conclusion this paper introduces tablellm (8b) tailored for tabular data manipulation in real office scenarios. we gather actual requirements from office settings and identify document-embedded and spreadsheet-embedded scenarios. ensuring highquality data through extended reasoning processes and cross-way validation on automatically gener-ated training data, the resulting tablellm performs comparably to gpt-4o and surpasses it in the spreadsheet-embedded scenario. we anticipate that our dataset, model checkpoint, and code will provide a cost-effective solution for enhancing llm capabilities for tables.