SyllabusQA: A Course Logistics Question Answering Dataset

Nigel Fernandez, Alexander Scarlatos, Andrew Lan

Abstract
Automated teaching assistants and chatbots have significant potential to reduce the workload of human instructors, especially for logistics-related question answering, which is important to students yet repetitive for instructors. However, due to privacy concerns, there is a lack of publicly available datasets. We introduce SyllabusQA, an open-source dataset with 63 real course syllabi covering 36 majors, containing 5,078 open-ended course logistics-related question-answer pairs that are diverse in both question types and answer formats. Since many logistics-related questions contain critical information like the date of an exam, it is important to evaluate the factuality of answers. We benchmark several strong baselines on this task, from large language model prompting to retrieval-augmented generation. We introduce Fact-QA, an LLM-based (GPT-4) evaluation metric to evaluate the factuality of predicted answers. We find that despite performing close to humans on traditional metrics of textual similarity, there remains a significant gap between automated approaches and humans in terms of fact precision.
Anthology ID:
2024.acl-long.557
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10344–10369
Language:
URL:
https://aclanthology.org/2024.acl-long.557
DOI:
10.18653/v1/2024.acl-long.557
Bibkey:
Cite (ACL):
Nigel Fernandez, Alexander Scarlatos, and Andrew Lan. 2024. SyllabusQA: A Course Logistics Question Answering Dataset. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10344–10369, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
SyllabusQA: A Course Logistics Question Answering Dataset (Fernandez et al., ACL 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2024.acl-long.557.pdf
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