# Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset

Source: [Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset](<https://www.sota2.com/research/paper/68fa76161e209131cea6b545>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:47:33.330Z

Authors: Tilahun Abedissa Taffa, Debayan Banerjee, Yaregal Assabie, Ricardo Usbeck

Published: 2024-12-03T19:37:00.000Z

## Abstract

Existing Scholarly Question Answering (QA) methods typically target homogeneous data sources, relying solely on either text or Knowledge Graphs (KGs). However, scholarly information often spans heterogeneous sources, necessitating the development of QA systems that integrate information from multiple heterogeneous data sources. To address this challenge, we introduce Hybrid-SQuAD (Hybrid Scholarly Question Answering Dataset), a novel large-scale QA dataset designed to facilitate answering questions incorporating both text and KG facts. The dataset consists of 10.5K question-answer pairs generated by a large language model, leveraging the KGs DBLP and SemOpenAlex alongside corresponding text from Wikipedia. In addition, we propose a RAG-based baseline hybrid QA model, achieving an exact match score of 69.65 on the Hybrid-SQuAD test set.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.02788>)

[PDF](<https://arxiv.org/pdf/2412.02788.pdf>)