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.

翻译：本文介绍了交互式表格（iTBLS），这是一个基于科学论文表格的交互式对话数据集。该数据集旨在通过AI驱动的多任务表格能力促进人机协作解决问题。与先前将交互建模为事实性问答或程序合成的研究不同，iTBLS将交互范围扩展至数学推理、自然语言操作以及对现有表格的自然语言扩展，通过将交互划分为三类任务——解释、修改或生成——来界定交互范围。此外，本文提出了一套针对iTBLS的基线方法，利用零样本提示和参数高效微调以适应不同计算场景。我们还引入了一种新颖的多步方法，并展示了如何将其与参数高效微调相结合，在iTBLS上实现最先进性能：在解释任务上比标准参数高效微调提升高达15%，修改任务提升18%，生成任务提升38%。