References & Citations
Computer Science > Computation and Language
Title: TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
(Submitted on 28 Mar 2024 (v1), last revised 1 Apr 2024 (this version, v2))
Abstract: We introduce TableLLM, a robust large language model (LLM) with 13 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 a benchmark 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.Our codes and data are publicly available at this https URL
Submission history
From: Bohan Zhang [view email][v1] Thu, 28 Mar 2024 11:21:12 GMT (3244kb,D)
[v2] Mon, 1 Apr 2024 05:10:56 GMT (3244kb,D)
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