SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing

Heidi C. Zhang, Sina J. Semnani, Farhad Ghassemi, Jialiang Xu, Shicheng Liu +1 more
6/1/2024
cs.CL

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Abstract

We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes, a hybrid question-answering (QA) pipeline that utilizes information from heterogeneous knowledge sources, including knowledge base, text, tables, and infoboxes. Our LLM-augmented approach achieves state-of-the-art performance on the Compmix dataset, the most comprehensive heterogeneous open-domain QA dataset, with 56.5% exact match (EM) rate. More importantly, manual analysis on a sample of the dataset suggests that SPAGHETTI is more than 90% accurate, indicating that EM is no longer suitable for assessing the capabilities of QA systems today.

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Cite this paper

@article{zhang2024spaghetti,
  title  = {SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing},
  author = {Heidi C. Zhang and Sina J. Semnani and Farhad Ghassemi and Jialiang Xu and Shicheng Liu and Monica S. Lam},
  year   = {2024},
  eprint = {2406.00562},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.00562v1}
}

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