@inproceedings{glenn-etal-2024-blendsql,
    title = "{B}lend{SQL}: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra",
    author = "Glenn, Parker  and
      Dakle, Parag  and
      Wang, Liang  and
      Raghavan, Preethi",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.findings-acl.25/",
    doi = "10.18653/v1/2024.findings-acl.25",
    pages = "453--466",
    abstract = "Many existing end-to-end systems for hybrid question answering tasks can often be boiled down to a {\textquotedblleft}prompt-and-pray{\textquotedblright} 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."
}