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

Paula Maddigan; Teo Susnjak

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

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arxiv 2303.14292 v1 pith:PMSDPNC2 submitted 2023-03-24 cs.HC

Paula Maddigan , Teo Susnjak This is my paper · ORCID

classification cs.HC

keywords languagedatachat2visnaturalnl2visdemonstratedlargemodels

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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.

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Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture
cs.AI 2024-12 reject novelty 3.0 of 10

Text2Insight combines an LLM text-to-SQL step with a rule-based chart predictor and BERT-based question answering and prediction, but its end-to-end performance claims rest on circular or missing evaluation.

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