Related papers: LLM-PQA: LLM-enhanced Prediction Query Answering

LLM-PQA: LLM-enhanced Prediction Query Answering

URL: http://arxiv.org/abs/2409.01140v1
Date: Mon, 2 Sep 2024 10:20:35 GMT
Title: LLM-PQA: LLM-enhanced Prediction Query Answering
Authors: Ziyu Li, Wenjie Zhao, Asterios Katsifodimos, Rihan Hai,
Abstract summary: This paper introduces LLM-PQA, a novel tool that addresses prediction queries formulated in natural language. This integration provides users with access to a vast spectrum of heterogeneous data and diverse ML models, facilitating dynamic prediction query answering.
Score: 7.346989832385652
License: http://creativecommons.org/licenses/by/4.0/
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.

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