SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing
arXiv preprint arXiv:2406.00562, 2024•arxiv.org
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 …
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 …
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.
arxiv.org