  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
https://github.com/TableLLM/TableLLM.
