This paper introduces Interactive Tables (iTBLS), a dataset of interactive conversations situated in tables from scientific articles. This dataset is designed to facilitate human-AI collaborative problem-solving through AI-powered multi-task tabular capabilities. In contrast to prior work that models interactions as factoid QA or procedure synthesis, iTBLS broadens the scope of interactions to include mathematical reasoning, natural language manipulation, and expansion of existing tables from natural language conversation by delineating interactions into one of three tasks: interpretation, modification, or generation. Additionally, the paper presents a suite of baseline approaches to iTBLS, utilizing zero-shot prompting and parameter-efficient fine-tuning for different computing situations. We also introduce a novel multi-step approach and show how it can be leveraged in conjunction with parameter-efficient fine-tuning to achieve the state-of-the-art on iTBLS; outperforming standard parameter-efficient fine-tuning by up to 15% on interpretation, 18% on modification, and 38% on generation.

本研究介绍了Interactive Tables (iTBLS)，这是一种包含科学文章中表格的交互式对话数据集，旨在通过AI支持的多任务表格能力促进人工智能与人类的协作问题解决。相较于先前将交互建模为事实型问答或过程合成的工作，iTBLS将交互的范围扩展到包括数学推理、自然语言操作以及通过将交互细分为解释、修改或生成三个任务，扩展现有表格的自然语言对话。此外，本文还介绍了一套基准方案来处理iTBLS，利用零样本提示和适用于不同计算情况的参数高效微调。我们还引入了一种新颖的多步骤方法，并展示了如何与参数高效微调结合使用，以在iTBLS上实现最新的成果；在解释、修改和生成方面，我们表现优于标准的参数高效微调方法，分别提高了15％、18％和38％。

iTBLS：一个涉及表格信息的互动对话数据集