ProTrix: Building Models for Planning and Reasoning over Tables with
  Sentence Context
Zirui Wu Yansong Feng
Tables play a crucial role in conveying information in various domains,
serving as indispensable tools for organizing and presenting data in a
structured manner. 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. We construct an
instruction tuning set TrixInstruct following the framework. Our dataset cover
queries that are program-unsolvable or need combining information from tables
and sentences to obtain planning and reasoning abilities. We present ProTrix by
finetuning Llama-2-7B on TrixInstruct. Our experiments show that ProTrix
generalizes to diverse tabular tasks and achieves comparable performance to
GPT-3.5-turbo. 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 will
release our dataset and model at https://github.com/WilliamZR/ProTrix.