  Semi-structured tables are ubiquitous. There has been a variety of tasks that
aim to automatically interpret, augment, and query tables. Current methods
often require pretraining on tables or special model architecture design, are
restricted to specific table types, or have simplifying assumptions about
tables and tasks. This paper makes the first step towards developing
open-source large language models (LLMs) as generalists for a diversity of
table-based tasks. Towards that end, we construct TableInstruct, a new dataset
with a variety of realistic tables and tasks, for instruction tuning and
evaluating LLMs. We further develop the first open-source generalist model for
tables, TableLlama, by fine-tuning Llama 2 (7B) with LongLoRA to address the
long context challenge. We experiment under both in-domain setting and
out-of-domain setting. On 7 out of 8 in-domain tasks, TableLlama achieves
comparable or better performance than the SOTA for each task, despite the
latter often has task-specific design. On 6 out-of-domain datasets, it achieves
5-44 absolute point gains compared with the base model, showing that training
on TableInstruct enhances the model's generalizability. We open-source our
dataset and trained model to boost future work on developing open generalist
models for tables.
