  Tables play a crucial role in conveying information in various domains. We
propose a Plan-then-Reason framework to answer different types of user queries
over tables with sentence context. The framework first plans the reasoning
paths over the context, then assigns each step to program-based or textual
reasoning to reach the final answer. This framework enhances the table
reasoning abilities for both in-context learning and fine-tuning methods.
GPT-3.5-Turbo following Plan-then-Reason framework surpasses other prompting
baselines without self-consistency while using less API calls and in-context
demonstrations. We also construct an instruction tuning set TrixInstruct to
evaluate the effectiveness of fine-tuning with this framework. We present
ProTrix model family by finetuning models on TrixInstruct. Our experiments show
that ProTrix family generalizes to diverse unseen tabular tasks with only 6k
training instances. We further demonstrate that ProTrix can generate accurate
and faithful explanations to answer complex free-form questions. Our work
underscores the importance of the planning and reasoning abilities towards a
model over tabular tasks with generalizability and interpretability. We
open-source our dataset and models at https://github.com/WilliamZR/ProTrix.
