@inproceedings{saha-etal-2024-analytics,
title = "Analytics Graph Query Solver ({AGQS}): Transforming Natural Language Queries into Actionable Insights",
author = "Saha, Debojyoti and
Singh, Krishna and
Mahato, Moushumi and
Nabi, Javaid",
editor = "Lalitha Devi, Sobha and
Arora, Karunesh",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-1.63/",
pages = "537--553",
abstract = "In today`s era, data analytics is crucial because it allows organizations to make informed decisions based on the analysis of large amounts of data. The evolving landscape of data analytics presents a growing challenge in effectively translating natural language queries into actionable insights. To address this challenge, we introduce a novel system that seamlessly integrates natural language processing (NLP), graph-based feature representation, and code generation. Our method, called Analytics Graph Query Solver (AGQS), utilizes large language models (LLMs) to construct a dynamic graph representing keywords and engineered features. AGQS transforms textual input queries into structured descriptions and generates corresponding plans. These plans are executed stepwise to create a unified code, which is subsequently applied to our in-house virtual assistant dataset to fulfill the user`s query. Furthermore, a robust verification module ensures the reliability of the obtained results. Through experimentation, our system achieved an accuracy of 62.2{\%}, outperforming models like GPT-4 (50.2{\%}), Graph Reader (56.6{\%}), Mistral3 7B (38.6{\%}), and Llama 7B (37.6{\%}). Overall, our approach highlights the importance of feature generation in textual query resolution and demonstrates notable improvements in accessibility and precision for data analytics. With this method, we aim to present a solution for converting natural language queries into actionable steps, ultimately generating code that provides data insights. This approach can be utilized across different datasets, empowering developers and researchers to gain valuable insights effortlessly."
}
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<abstract>In today‘s era, data analytics is crucial because it allows organizations to make informed decisions based on the analysis of large amounts of data. The evolving landscape of data analytics presents a growing challenge in effectively translating natural language queries into actionable insights. To address this challenge, we introduce a novel system that seamlessly integrates natural language processing (NLP), graph-based feature representation, and code generation. Our method, called Analytics Graph Query Solver (AGQS), utilizes large language models (LLMs) to construct a dynamic graph representing keywords and engineered features. AGQS transforms textual input queries into structured descriptions and generates corresponding plans. These plans are executed stepwise to create a unified code, which is subsequently applied to our in-house virtual assistant dataset to fulfill the user‘s query. Furthermore, a robust verification module ensures the reliability of the obtained results. Through experimentation, our system achieved an accuracy of 62.2%, outperforming models like GPT-4 (50.2%), Graph Reader (56.6%), Mistral3 7B (38.6%), and Llama 7B (37.6%). Overall, our approach highlights the importance of feature generation in textual query resolution and demonstrates notable improvements in accessibility and precision for data analytics. With this method, we aim to present a solution for converting natural language queries into actionable steps, ultimately generating code that provides data insights. This approach can be utilized across different datasets, empowering developers and researchers to gain valuable insights effortlessly.</abstract>
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%0 Conference Proceedings
%T Analytics Graph Query Solver (AGQS): Transforming Natural Language Queries into Actionable Insights
%A Saha, Debojyoti
%A Singh, Krishna
%A Mahato, Moushumi
%A Nabi, Javaid
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F saha-etal-2024-analytics
%X In today‘s era, data analytics is crucial because it allows organizations to make informed decisions based on the analysis of large amounts of data. The evolving landscape of data analytics presents a growing challenge in effectively translating natural language queries into actionable insights. To address this challenge, we introduce a novel system that seamlessly integrates natural language processing (NLP), graph-based feature representation, and code generation. Our method, called Analytics Graph Query Solver (AGQS), utilizes large language models (LLMs) to construct a dynamic graph representing keywords and engineered features. AGQS transforms textual input queries into structured descriptions and generates corresponding plans. These plans are executed stepwise to create a unified code, which is subsequently applied to our in-house virtual assistant dataset to fulfill the user‘s query. Furthermore, a robust verification module ensures the reliability of the obtained results. Through experimentation, our system achieved an accuracy of 62.2%, outperforming models like GPT-4 (50.2%), Graph Reader (56.6%), Mistral3 7B (38.6%), and Llama 7B (37.6%). Overall, our approach highlights the importance of feature generation in textual query resolution and demonstrates notable improvements in accessibility and precision for data analytics. With this method, we aim to present a solution for converting natural language queries into actionable steps, ultimately generating code that provides data insights. This approach can be utilized across different datasets, empowering developers and researchers to gain valuable insights effortlessly.
%U https://aclanthology.org/2024.icon-1.63/
%P 537-553
Markdown (Informal)
[Analytics Graph Query Solver (AGQS): Transforming Natural Language Queries into Actionable Insights](https://aclanthology.org/2024.icon-1.63/) (Saha et al., ICON 2024)
ACL