SYLLABUSQA: A Course Logistics Question Answering Dataset

Core Concepts

Automated teaching assistants have the potential to reduce human workload in logistics-related question answering, but there is a gap in factuality precision between automated approaches and humans.

Abstract

Introduction to SYLLABUSQA dataset for course logistics-related QA.
Importance of factuality evaluation in QA tasks.
Benchmarking strong baselines on SYLLABUSQA.
Challenges faced by LLM-based approaches in answering questions accurately.
Future work and conclusions on improving automated teaching assistants.

Stats

"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."
"There are 2,177 Explicit QA pairs, 2,181 Implicit pairs, and 720 Insufficient Information pairs."
"Question type prediction accuracy is at 54%."

Quotes

Key Insights Distilled From

SyllabusQA

by Nigel Fernan... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14666.pdf

Deeper Inquiries

How can automated teaching assistants be improved to bridge the gap in factuality precision compared to humans?

Automated teaching assistants can be enhanced in several ways to improve factuality precision and narrow the gap compared to human performance. One approach is to incorporate more advanced retrieval methods, such as dense passage retrieval, which can help retrieve relevant information from course materials more accurately. By training models on a larger and more diverse dataset like SYLLABUSQA, they can learn to generate answers that are not only textually similar but also factually accurate. Additionally, leveraging question meta-information and implementing overgenerate-and-rank strategies can aid in improving robustness and accuracy of responses.

What ethical considerations should be taken into account when deploying automated teaching assistants in educational settings?

When deploying automated teaching assistants in educational settings, it is crucial to consider various ethical aspects. Firstly, ensuring diversity among annotators during data collection helps mitigate bias present in the dataset. Transparency about how student data is used and protected is essential for maintaining privacy and trust with users. It's important that these systems are used for research purposes initially before being deployed widely in classrooms after thorough risk assessment. Moreover, using open-source models rather than closed-source ones like GPT-4 ensures transparency and reduces potential risks associated with proprietary technologies.

How can the SYLLABUSQA dataset be expanded to include courses taught in languages other than English?

Expanding the SYLLABUSQA dataset to include courses taught in languages other than English involves several steps. Initially, collecting syllabi from universities where courses are conducted primarily in different languages would provide a foundation for creating multilingual datasets. Annotating QA pairs based on these syllabi by recruiting bilingual or native speakers proficient in both languages ensures accurate translations while maintaining context relevance. Implementing language-specific preprocessing techniques for parsing text formats unique to each language will further enhance the quality of the dataset across various linguistic contexts.