LLM-PQA: LLM-enhanced Prediction Query Answering
<|reference_start|>LLM-PQA: LLM-enhanced Prediction Query Answering: 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.<|reference_end|>
@article{li2024llm-pqa:,
    title={LLM-PQA: LLM-enhanced Prediction Query Answering},
    author={Ziyu Li, Wenjie Zhao, Asterios Katsifodimos, Rihan Hai},
    journal={arXiv preprint arXiv:2409.01140},
    year={2024},
    doi={10.1145/3627673.3679210},
    archivePrefix={arXiv},
    eprint={2409.01140},
    primaryClass={cs.IR cs.LG}
}