A Comparative Study of Decoding Strategies in Medical Text Generation
- 10 February 2026
- book chapter
- Published by Springer Nature
- p. 1-15
Abstract
No abstract availableKeywords
All Related Versions
This publication has 15 references indexed in Scilit:
- A survey of large language models for healthcare: from data, technology, and applications to accountability and ethicsInformation Fusion, 2025
- Towards AI-Driven Healthcare: Systematic Optimization, Linguistic Analysis, and Clinicians’ Evaluation of Large Language Models for Smoking Cessation Interventions2024
- The model student: GPT-4 performance on graduate biomedical science examsScientific Reports, 2024
- A Thorough Examination of Decoding Methods in the Era of LLMs2024
- Opportunities and challenges for ChatGPT and large language models in biomedicine and healthBriefings in Bioinformatics, 2023
- Locally Typical SamplingTransactions of the Association for Computational Linguistics, 2023
- On Decoding Strategies for Neural Text GeneratorsTransactions of the Association for Computational Linguistics, 2022
- Truncation Sampling as Language Model Desmoothing2022
- Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data2020
- Diverse Beam Search for Improved Description of Complex ScenesProceedings of the AAAI Conference on Artificial Intelligence, 2018