Interactive Table Synthesis With Natural Language | Semantic Scholar (2024)

This paper presents a suite of baseline approaches to iTBLS, utilizing zero-shot prompting and parameter-efficient fine-tuning for different computing situations, and introduces a novel multi-step approach that can be leveraged in conjunction with parameter-efficient fine-tuning to achieve the state-of-the-art on iTBLS.

The findings from the user study reveal that WaitGPT facilitates monitoring and steering of data analysis performed by LLMs, enabling participants to enhance error detection and increase their overall confidence in the results.

NL4DV is a Python package that takes as input a tabular dataset and a natural language query about that dataset and returns an analytic specification modeled as a JSON object containing data attributes, analytic tasks and a list of Vega-Lite specifications relevant to the input query.

Eviza provides a natural language interface for an interactive query dialog with an existing visualization rather than starting from a blank sheet and asking closed-ended questions that return a single text answer or static visualization.

This paper introduces a structured representation of users' visualization editing intents, called editing actions, based on a formative study and an extensive survey on visualization construction tools, and implements a deep learning-based NL interpreter to translate NL utterances into editing actions.

A model to proactively suggest data transforms which map input data to a relational format expected by analysis tools is presented, and a metric that scores tables according to type hom*ogeneity, sparsity and the presence of delimiters is proposed.

The design and implementation of a robust natural language based interface to spreadsheet programming that supports a rich user interaction model including annotating the user's natural language specification and explaining the synthesized DSL programs by paraphrasing them into structured English is described.

This work augments a state-of-the-art NL interface over web tables, enhancing it in both its training and deployment phase, and introduces novel query explanations that provide a graphic representation of the query cell-based provenance in its execution on a given table.

Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table, in contrast to existing systems that transform data by composing operations.

This work designs FlowSense, a natural language interface for dataflow visualization systems that utilizes state-of-the-art natural language processing techniques to assist dataflow diagram construction and applies FlowSense to the VisFlow subset-flow visualization system to enhance its usability.

Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction, that enables rapid specification of interactive data visualizations.

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