iTBLS: A Dataset of Interactive Conversations Over Tabular Information

• URL: http://arxiv.org/abs/2404.12580v2
• Date: Tue, 19 Aug 2025 01:43:41 GMT
• Title: iTBLS: A Dataset of Interactive Conversations Over Tabular Information
• Authors: Anirudh Sundar, Christopher Richardson, Adar Avsian, Larry Heck,
• Abstract summary: The iTBLS dataset consists of three types of tabular tasks -- interpretation, modification, and generation.<n>The paper presents a novel framework that reformulates tabular operations as question-answering.<n>The developed approach results in an improvement on all tasks on a sequence-to-sequence modeling baseline on iTBLS.<n>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.
• Score: 2.9665568096804846
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Scalable Representation Learning for Multimodal Tabular Transactions [14.18267117657451]
  We present an innovative and scalable solution to these challenges. We propose a parameter efficient decoder that interleaves transaction and text modalities. We validate the efficacy of our solution on a large-scale dataset of synthetic payments transactions.
  arXiv  Detail & Related papers  (2024-10-10T12:18:42Z)
• Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction [1.0968343822308813]
  This paper proposes a novel approach that extracts triples straightforward from tabular data and integrates it with a retrieval-augmented generation (RAG) model to enhance the accuracy, coherence, and contextual richness of responses generated by a fine-tuned GPT-3.5-turbo-0125 model. Our approach significantly outperforms existing baselines on the FeTaQA dataset, particularly excelling in Sacre-BLEU and ROUGE metrics.
  arXiv  Detail & Related papers  (2024-09-21T16:46:15Z)
• Evaluation of Table Representations to Answer Questions from Tables in Documents : A Case Study using 3GPP Specifications [0.650923326742559]
  The representation of a table in terms of what is a relevant chunk is not obvious. Row level representations with corresponding table header information being included in every cell improves the performance of the retrieval.
  arXiv  Detail & Related papers  (2024-08-30T04:40:35Z)
• H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables [56.73919743039263]
  This paper introduces a novel algorithm that integrates both symbolic and semantic (textual) approaches in a two-stage process to address limitations. Our experiments demonstrate that H-STAR significantly outperforms state-of-the-art methods across three question-answering (QA) and fact-verification datasets.
  arXiv  Detail & Related papers  (2024-06-29T21:24:19Z)
• Making Pre-trained Language Models Great on Tabular Prediction [50.70574370855663]
  The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. We present TP-BERTa, a specifically pre-trained LM for tabular data prediction. A novel relative magnitude tokenization converts scalar numerical feature values to finely discrete, high-dimensional tokens, and an intra-feature attention approach integrates feature values with the corresponding feature names.
  arXiv  Detail & Related papers  (2024-03-04T08:38:56Z)
• Guiding Language Model Reasoning with Planning Tokens [122.43639723387516]
  Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks. We propose a hierarchical generation scheme to encourage a more structural generation of chain-of-thought steps. Our approach requires a negligible increase in trainable parameters (0.001%) and can be applied through either full fine-tuning or a more parameter-efficient scheme.
  arXiv  Detail & Related papers  (2023-10-09T13:29:37Z)
• Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity [68.8204255655161]
  We present the semantic notion of agentivity as a case study for probing such interactions. This suggests LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery.
  arXiv  Detail & Related papers  (2023-05-29T16:24:01Z)
• QTSumm: Query-Focused Summarization over Tabular Data [58.62152746690958]
  People primarily consult tables to conduct data analysis or answer specific questions. We define a new query-focused table summarization task, where text generation models have to perform human-like reasoning. We introduce a new benchmark named QTSumm for this task, which contains 7,111 human-annotated query-summary pairs over 2,934 tables.
  arXiv  Detail & Related papers  (2023-05-23T17:43:51Z)
• Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach [60.03002572791552]
  We propose a table structure understanding and text deliberating approach, namely TASD. Specifically, we devise a three-layered multi-head attention network to realize the table-structure-aware text generation model. Our approach can generate faithful and fluent descriptive texts for different types of tables.
  arXiv  Detail & Related papers  (2023-01-05T14:03:26Z)
• Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework [5.351873055148804]
  Self-training framework generates diverse synthetic data with complex logic. We optimize the procedure using a "Table-Text Manipulator" to handle joint table-text reasoning scenarios. UCTRST achieves above 90% of the supervised model performance on different tasks and domains.
  arXiv  Detail & Related papers  (2022-12-20T09:15:03Z)
• SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning [5.5616364225463055]
  We introduce a new framework, Subsetting features of Tabular data (SubTab) In this paper, we introduce a new framework, Subsetting features of Tabular data (SubTab) We argue that reconstructing the data from the subset of its features rather than its corrupted version in an autoencoder setting can better capture its underlying representation.
  arXiv  Detail & Related papers  (2021-10-08T20:11:09Z)
• TABBIE: Pretrained Representations of Tabular Data [22.444607481407633]
  We devise a simple pretraining objective that learns exclusively from tabular data. Unlike competing approaches, our model (TABBIE) provides embeddings of all table substructures. A qualitative analysis of our model's learned cell, column, and row representations shows that it understands complex table semantics and numerical trends.
  arXiv  Detail & Related papers  (2021-05-06T11:15:16Z)
• Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing [52.24507547010127]
  Cross-domain context-dependent semantic parsing is a new focus of research. We present a dynamic graph framework that effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds. The proposed framework outperforms all existing models by large margins, achieving new state-of-the-art performance on two large-scale benchmarks.
  arXiv  Detail & Related papers  (2021-01-05T18:11:29Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)
• TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data [113.29476656550342]
  We present TaBERT, a pretrained LM that jointly learns representations for NL sentences and tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. Implementation of the model will be available at http://fburl.com/TaBERT.
  arXiv  Detail & Related papers  (2020-05-17T17:26:40Z)
• ToTTo: A Controlled Table-To-Text Generation Dataset [61.83159452483026]
  ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples. We introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. While usually fluent, existing methods often hallucinate phrases that are not supported by the table.
  arXiv  Detail & Related papers  (2020-04-29T17:53:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.