用大模型+检索增强实现自然语言预测查询的动态回答
LLM-PQA: LLM-enhanced Prediction Query Answering
- 结合大模型与检索增强，接入数据湖和模型库
- 支持按需动态训练模型，无预训练模型也可响应
- 适合需要灵活处理预测查询的研究与工程人员
大型语言模型（LLMs）为改变查询处理方式提供了新可能，突破传统SQL数据库的限制。然而，利用LLM回答预测查询仍具挑战性，因需调用外部机器学习模型并执行推理。本文提出LLM-PQA，首个将LLM能力与检索增强机制结合用于自然语言预测查询的工具。通过整合数据湖和模型库，用户可访问异构数据与多样化的机器学习模型，实现动态预测查询回答。此外，LLM-PQA可根据具体查询需求动态训练模型，即使模型库中无适配预训练模型，也能保证结果可靠且相关。
原文摘要 · Abstract (English)
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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