Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework
Large language models (LLMs) are increasingly adopted in medical question answering (QA) scenarios. However, LLMs have been proven to generate hallucinations and nonfactual information, undermining their trustworthiness in high-stakes medical tasks. Conformal Prediction (CP) is now recognized as a r...
Saved in:
| Main Authors: | Yusong Ke, Hongru Lin, Yuting Ruan, Junya Tang, Li Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/9/1538 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing Visual Question Answering for Multiple Choice Questions
by: Rashi Goel, et al.
Published: (2025-01-01) -
The battle of question formats: a comparative study of retrieval practice using very short answer questions and multiple choice questions
by: Elise V. van Wijk, et al.
Published: (2024-12-01) -
Generative Models for Multiple-Choice Question Answering in Portuguese: A Monolingual and Multilingual Experimental Study
by: Guilherme Dallmann Lima, et al.
Published: (2025-05-01) -
The pearls and pitfalls of setting high-quality multiple choice questions for clinical medicine
by: Mergan Naidoo
Published: (2023-05-01) -
Multimodal representative answer extraction in community question answering
by: Ming Li, et al.
Published: (2023-10-01)