# LLM-PQA: LLM-enhanced Prediction Query Answering

arXiv: https://arxiv.org/abs/2409.01140 · HTML: https://mlsanity.com/paper/2409.01140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Model Selection with Model Zoo via Graph Learning](https://mlsanity.com/paper/2404.03988/) (2024)
- [Amalur: Data Integration Meets Machine Learning](https://mlsanity.com/paper/2205.09681/) (2022)

## Abstract (arXiv)

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.