BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra
<|reference_start|>BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra: 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 https://github.com/parkervg/blendsql.<|reference_end|>
@article{glenn2024blendsql:,
    title={BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in
  Relational Algebra},
    author={Parker Glenn, Parag Pravin Dakle, Liang Wang, Preethi Raghavan},
    journal={arXiv preprint arXiv:2402.17882},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17882},
    primaryClass={cs.CL}
}