Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset

Tilahun Abedissa Taffa, Debayan Banerjee, Yaregal Assabie, Ricardo Usbeck
12/3/2024
cs.CLcs.AI

No confident code implementation found for this paper. 5 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Hybrid Graphs for Table-and-Text based Question Answering using LLMs

84% match2025

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

83% match2024

ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering

81% match2024

RAG-based Question Answering over Heterogeneous Data and Text

81% match2024

Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models

81% match2024

Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway

81% match1 repo52024

Cite this paper

@article{taffa2024hybridsquad,
  title  = {Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset},
  author = {Tilahun Abedissa Taffa and Debayan Banerjee and Yaregal Assabie and Ricardo Usbeck},
  year   = {2024},
  eprint = {2412.02788},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.02788v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2412.02788.svg)](https://codeofpaper.com/paper/2412.02788)

Discussion