Many existing end-to-end systems for hybrid question answering tasks can
often be boiled down to a "prompt-and-pray" paradigm, where the user has
limited control and insight into the intermediate reasoning steps used to
achieve the final result. Additionally, due to the context size limitation of
many transformer-based LLMs, it is often not reasonable to expect that the full
structured and unstructured context will fit into a given prompt in a zero-shot
setting, let alone a few-shot setting. We introduce BlendSQL, a superset of
SQLite to act as a unified dialect for orchestrating reasoning across both
unstructured and structured data. For hybrid question answering tasks involving
multi-hop reasoning, we encode the full decomposed reasoning roadmap into a
single interpretable BlendSQL query. Notably, we show that BlendSQL can scale
to massive datasets and improve the performance of end-to-end systems while
using 35% fewer tokens. Our code is available and installable as a package at
this https URL

BlendSQL 是一个 SQLite 的超集，通过单个解释性的 BlendSQL 查询将完整的分解推理路线编码为一个统一的方言，可用于解决涉及多跳推理的混合问答任务，并且能够在使用更少词汇的情况下扩展到大型数据集并提高端到端系统的性能。