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 this https URL

我们提出了一个计划然后推理的框架，用来回答带有句子上下文的不同类型的用户查询，首先计划上下文中的推理路径，然后将每个步骤分配给基于程序或文本的推理，以得出最终答案。我们构建了一个遵循该框架的指令调整集 TrixiInstruct，我们的数据集涵盖了需要计划和推理能力的程序不可解决的查询或需要结合来自表格和句子的信息的查询。通过在 TrixiInstruct 上微调 Llama-2-7B，我们提出了 ProTrix。我们的实验表明 ProTrix 的泛化性能能应用于各种表格任务，并且与 GPT-3.5-turbo 的性能相当。我们进一步证明了 ProTrix 能够生成准确和可靠的解释来回答复杂的自由形式问题。我们的工作强调了在具有泛化性和可解释性的表格任务模型中的计划和推理能力的重要性。我们将在此 https URL 发布我们的数据集和模型。