i TBLS : A Dataset of Interactive Conversations Over Tabular Information (2025)

Abstract: This paper introduces Interactive Tables (iTBLS), a dataset of interactive conversations that focuses on natural-language manipulation of tabular information sourced from academic pre-prints on ArXiv. The iTBLS dataset consists of three types of tabular tasks – interpretation, modification, and generation. Interpretation focuses on tabular understanding, modification focuses on manipulating tabular information, and generation focuses on the addition of new natural-language evidence. In addition, the paper presents a novel framework that reformulates tabular operations as question-answering, where an appropriate question is formulated based on the nature of interaction and the question is answered using the user request as evidence. The developed approach results in an improvement on all tasks on a sequence-to-sequence modeling baseline on iTBLS. In addition, the question-answering-based reformulation is applied to datasets from prior work for the text-to-table task where textual paragraphs are summarized into tables. The novel approach results in up to 13% improvement in Exact-Match accuracy and up to 16% improvement in BERTScores compared to the prior state-of-the-art.

## introduction recent research on conversational ai has focused on adding enhanced multi-task capabilities to large language models (llms). this research includes building systems capable of situated interactions over structured knowledge sources such as tabular information @xcite . automated methods for tabular interpretation, manipulation, and generation empower users by saving time and reducing errors in managing tabular content @xcite . previous studies have focused on individual aspects of tabular data management: representation learning for interpretation tasks like * work done while at georgia tech grounded question answering, manipulation for data wrangling, and generation for summarizing textual information independently @xcite @xcite . the development of situated conversational interactions over tables necessitates a suite of approaches to unify tabular interpretation, modification, and generation in a conversational context. additionally, an important yet largely unaddressed challenge in interacting with tabular sources is the ability to modify existing tabular content using conversational natural language commands. to address these challenges, this paper introduces interactive tables (itbls) foot_0 , a dataset of interactive conversations in english situated in tabular information. itbls decomposes the challenge into three distinct tasks: interpretation, which involves understanding tabular content within a conversational framework; modification, which entails manipulating tabular content through natural language commands; and generation, which focuses on integrating new natural language information into existing tables. the tabular information in itbls is sourced from scientific articles hosted on arxiv foot_1 , an open-access repository of academic preprints. beyond factoid question-answering, itbls encompasses tasks such as comparison, determining absolute and relative positions, and mathematical reasoning. previous research primarily examined procedural command generation for spreadsheets or the alignment of tabular data through llms. itbls integrates these functionalities into a unified task, enabling the manipulation of existing tables through natural-language commands. on tabular generation, while prior work addressed the summarization of natural language paragraphs in a tabular format, itbls focuses on generating row or column data conversationally. in addition to building itbls, this paper develops a novel approach to address tabular operations by reformulating the task as conditional question answering. furthermore, the question-answeringbased reformulation is applied to other datasets introduced in prior work @xcite and results in better performance in terms of both table-cell accuracy and bertscore. the contributions of this work are as follows: ## related work a detailed survey of llms for tabular data is available in @xcite . related work on paired natural-language and tabular data can be broadly classified by the nature of the interaction: tabular interpretation, tabular modification, and tabular generation. ## the itbls dataset the interactive tables (itbls) dataset features conversational interactions situated in tabular data, covering the three distinct types of interactions described in section 2: interpretation, modification, and generation. each example type is exemplified in figure 1 and described below. in addition, since the mode of interaction is not known a priori, any proposed approach using itbls must effectively identify the interaction type, either explicitly or implicitly. in the following sections, we provide a detailed description of each type of interaction and outline the dataset collection process.