Genicious: Contextual Few-shot Prompting for Insights Discovery

Data and insights discovery is critical for decision-making in modern organizations. We present Genicious, an LLM-aided interface that enables users to interact with tabular datasets and ask complex queries in natural language. By benchmarking various prompting strategies and language models, we have developed an end-to-end tool that leverages contextual few-shot prompting, achieving superior performance in terms of latency, accuracy, and scalability. Genicious empowers stakeholders to explore, analyze and visualize their datasets efficiently while ensuring data security through role-based access control and a Text-to-SQL approach.

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Published
2024-12-18
DOI
https://doi.org/10.1145/3703323.3704274
Primary Topic
Topic Modeling
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article
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Genicious: Contextual Few-shot Prompting for Insights Discovery

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Topic Modeling
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Genicious: Contextual Few-shot Prompting for Insights Discovery

Darshita Rathore, Vipasha Rana, Kanishka, Vineet Kumar, Ronald Tony, Bhuvanesh Mandora, Chetna Bansal, Anindya Moitra
article en

Abstract

Data and insights discovery is critical for decision-making in modern organizations. We present Genicious, an LLM-aided interface that enables users to interact with tabular datasets and ask complex queries in natural language. By benchmarking various prompting strategies and language models, we have developed an end-to-end tool that leverages contextual few-shot prompting, achieving superior performance in terms of latency, accuracy, and scalability. Genicious empowers stakeholders to explore, analyze and visualize their datasets efficiently while ensuring data security through role-based access control and a Text-to-SQL approach.

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Topic Modeling
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