LLM-PQA: LLM-enhanced Prediction Query Answering

TL;DR

LLM-PQA leverages large language models combined with retrieval mechanisms and dynamic model training to enhance prediction query answering in natural language, integrating data lakes and model zoos for flexible, on-demand predictions.

Contribution

It introduces the first system combining LLMs with retrieval-augmented mechanisms and dynamic model training for prediction queries.

Findings

Enables dynamic training of models on demand.

Integrates heterogeneous data sources and diverse ML models.

Provides reliable prediction answers even without pre-trained models.

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.