LLM-PQA: LLM-enhanced Prediction Query Answering

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Abstract

The advent of Large Language Models (LLMs) provides an opportunity to change the way queries are processed, moving beyond the constraints of conventional SQL-based database systems. However, using an LLM to answer a prediction query is still challenging, since an external ML model has to be employed and inference has to be performed in order to provide an answer. This paper introduces LLM-PQA, a novel tool that addresses prediction queries formulated in natural language. LLM-PQA is the first to combine the capabilities of LLMs and retrieval-augmented mechanism for the needs of prediction queries by integrating data lakes and model zoos. This integration provides users with access to a vast spectrum of heterogeneous data and diverse ML models, facilitating dynamic prediction query answering. In addition, LLM-PQA can dynamically train models on demand, based on specific query requirements, ensuring reliable and relevant results even when no pre-trained model in a model zoo, available for the task.

Original languageEnglish
Title of host publicationCIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Pages5239-5243
Number of pages5
ISBN (Electronic)9798400704369
DOIs
Publication statusPublished - 2024
Event33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 - Boise, United States
Duration: 21 Oct 202425 Oct 2024

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
ISSN (Print)2155-0751

Conference

Conference33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Country/TerritoryUnited States
CityBoise
Period21/10/2425/10/24

Keywords

  • data lake
  • large language models
  • model zoo
  • prediction query

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