Chat2VIS: Fine-Tuning Data Visualisations using Multilingual Natural Language Text and Pre-Trained Large Language Models

TL;DR

Chat2VIS is a novel system that uses large language models to generate data visualisations from multilingual natural language requests, improving accessibility and setting new benchmarks in NL2VIS technology.

Contribution

This paper introduces Chat2VIS, the first NL2VIS system capable of understanding multilingual requests and fine-tuning visualisations using pre-trained large language models.

Findings

Achieves high accuracy in visualisation generation from multilingual inputs

Demonstrates superior performance over previous NL2VIS systems

Provides quantitative benchmarks for NL2VIS evaluation

Abstract

The explosion of data in recent years is driving individuals to leverage technology to generate insights. Traditional tools bring heavy learning overheads and the requirement for understanding complex charting techniques. Such barriers can hinder those who may benefit from harnessing data for informed decision making. The emerging field of generating data visualisations from natural language text (NL2VIS) addresses this issue. This study showcases Chat2VIS, a state-of-the-art NL2VIS solution. It capitalises on the latest in AI technology with the upsurge in pre-trained large language models (LLMs) such as GPT-3, Codex, and ChatGPT. Furthermore, the rise in natural language interfaces (NLI) and chatbots is taking centre stage. This work illustrates how Chat2VIS leverages similar techniques to fine-tune data visualisation components beyond that demonstrated in previous approaches. In addition, this paper presents the flexibility of Chat2VIS to comprehend multilingual natural language requests. No other NL2VIS system has demonstrated this unique talent. In concluding, this research provides quantitative benchmarking evaluations to contribute to the paucity of NL2VIS standards.