Elimination-based reasoning with LLM for multiple-choice educational question answering
Abstract Large language models (LLMs) have made remarkable progress in question answering, but current approaches in the educational domain often directly predict an answer from multiple choices without thoroughly considering each option. This can lead to suboptimal performance, especially when dist...
Saved in:
| Main Authors: | Qianli Zhao, Mei Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00122-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrating LLM-based code optimization with human-like exclusionary reasoning for computational education
by: Yi Rong, et al.
Published: (2025-06-01) -
Evaluating large language models as graders of medical short answer questions: a comparative analysis with expert human graders
by: Olena Bolgova, et al.
Published: (2025-12-01) -
Cross-Encoder-Based Semantic Evaluation of Extractive and Generative Question Answering in Low-Resourced African Languages
by: Funebi Francis Ijebu, et al.
Published: (2025-03-01) -
Measuring the Feasibility of a Question and Answering System for the Sarawak Gazette Using Chatbot Technology
by: Yasir Lutfan bin Yusuf, et al.
Published: (2025-08-01) -
A lightweight knowledge graph-driven question answering system for field-based mineral resource survey
by: Mingguo Wang, et al.
Published: (2025-09-01)