Cover Image

Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Christophe, Clément, Raha, Tathagata, Maslenkova, Svetlana, Salman, Muhammad Umar, Kanithi, Praveen K, Pimentel, Marco AF, Khan, Shadab
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Med42-v2: A Suite of Clinical LLMs
by: Christophe, Clément, et al.
Published: (2024)

Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency
by: Maslenkova, Svetlana, et al.
Published: (2025)

Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks
by: Pimentel, Marco AF, et al.
Published: (2024)

Named Clinical Entity Recognition Benchmark
by: Abdul, Wadood M, et al.
Published: (2024)

MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications
by: Kanithi, Praveenkumar, et al.
Published: (2024)

Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
by: Saadi, Nada, et al.
Published: (2025)

Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
by: Christophe, Clément, et al.
Published: (2024)

Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation
by: Raha, Tathagata, et al.
Published: (2026)

Overalignment in Frontier LLMs: An Empirical Study of Sycophantic Behaviour in Healthcare
by: Christophe, Clément, et al.
Published: (2026)

Do Instruction-Tuned Models Always Perform Better Than Base Models? Evidence from Math and Domain-Shifted Benchmarks
by: Munjal, Prateek, et al.
Published: (2026)

SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training
by: Zhang, Gengwei, et al.
Published: (2024)

ObjectCompose: Evaluating Resilience of Vision-Based Models on Object-to-Background Compositional Changes
by: Malik, Hashmat Shadab, et al.
Published: (2024)

Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran
by: Salman, Muhammad Umar, et al.
Published: (2026)

Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem
by: Wang, Yubo, et al.
Published: (2025)

Can Muon Fine-tune Adam-Pretrained Models?
by: Qu, Xingyu, et al.
Published: (2026)

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
by: Zhou, Huichi, et al.
Published: (2025)

Debiasing LLMs by Fine-tuning
by: Gao, Zhenyu, et al.
Published: (2026)

PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning
by: Wang, Qibin, et al.
Published: (2024)

UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities
by: Khattak, Muhammad Uzair, et al.
Published: (2024)

Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology
by: Malik, Hashmat Shadab, et al.
Published: (2025)

The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging
by: Hirano, Masanori, et al.
Published: (2024)

SC-Phi2: A Fine-tuned Small Language Model for StarCraft II Macromanagement Tasks
by: Khan, Muhammad Junaid, et al.
Published: (2024)

MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining
by: Liu, Yunze, et al.
Published: (2024)

Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models
by: Malik, Hashmat Shadab, et al.
Published: (2025)

Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments
by: Ali, Muhammad, et al.
Published: (2025)

CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation
by: Ali, Muhammad, et al.
Published: (2024)

Underwater Object Detection Enhancement via Channel Stabilization
by: Ali, Muhammad, et al.
Published: (2024)

iREL at SemEval-2024 Task 9: Improving Conventional Prompting Methods for Brain Teasers
by: Gupta, Harshit, et al.
Published: (2024)

Towards Objective Fine-tuning: How LLMs' Prior Knowledge Causes Potential Poor Calibration?
by: Wang, Ziming, et al.
Published: (2025)

Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning
by: Huang, Shih-Cheng, et al.
Published: (2022)

Performance of Small Language Model Pretraining on FABRIC: An Empirical Study
by: Rao, Praveen
Published: (2026)

Chaos in Liquid Crystal Directrons
by: Singh, Praveen Kumar, et al.
Published: (2026)

Towards Evaluating the Robustness of Visual State Space Models
by: Malik, Hashmat Shadab, et al.
Published: (2024)

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder
by: Islam, K M Sajjadul, et al.
Published: (2025)

DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models
by: Kumar, Komal, et al.
Published: (2025)

Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning
by: Li, Jiachen, et al.
Published: (2023)

Fine-tuning can Help Detect Pretraining Data from Large Language Models
by: Zhang, Hengxiang, et al.
Published: (2024)

Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity
by: He, Mutian, et al.
Published: (2024)

Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!
by: Zhang, Zhexin, et al.
Published: (2025)

Incivility and Rigidity: Evaluating the Risks of Fine-Tuning LLMs for Political Argumentation
by: Churina, Svetlana, et al.
Published: (2024)