Cover Image

Supervised Fine-Tuning or In-Context Learning? Evaluating LLMs for Clinical NER

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Baroian, Andrei
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training
by: Baroian, Andrei, et al.
Published: (2025)

NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages
by: Abdullah, Abdulhady Abas, et al.
Published: (2024)

Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
by: Maggini, Michele Joshua, et al.
Published: (2025)

Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs
by: Lu, Yuxiao, et al.
Published: (2024)

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
by: Chen, Zihong, et al.
Published: (2025)

Supervised Fine-Tuning as Inverse Reinforcement Learning
by: Sun, Hao
Published: (2024)

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

Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
by: Rallapalli, Swati, et al.
Published: (2025)

2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion
by: Wang, Dongsheng, et al.
Published: (2024)

Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
by: Jin, Hongwei, et al.
Published: (2024)

Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025)

Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
by: Pang, Jinlong, et al.
Published: (2025)

Adapting LLMs for the Medical Domain in Portuguese: A Study on Fine-Tuning and Model Evaluation
by: Paiola, Pedro Henrique, et al.
Published: (2024)

Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue
by: Alghisi, Simone, et al.
Published: (2024)

Teaching LLMs How to Learn with Contextual Fine-Tuning
by: Choi, Younwoo, et al.
Published: (2025)

Fine-Tuning a Time Series Foundation Model with Wasserstein Loss
by: Chernov, Andrei
Published: (2024)

Lightweight Clinical Decision Support System using QLoRA-Fine-Tuned LLMs and Retrieval-Augmented Generation
by: Ansari, Mohammad Shoaib, et al.
Published: (2025)

NER4all or Context is All You Need: Using LLMs for low-effort, high-performance NER on historical texts. A humanities informed approach
by: Hiltmann, Torsten, et al.
Published: (2025)

LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach
by: Cheng, Xiang, et al.
Published: (2025)

Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study
by: Zhou, Yujun, et al.
Published: (2025)

Fine-Tuning LLMs for Reliable Medical Question-Answering Services
by: Anaissi, Ali, et al.
Published: (2024)

Boosting In-Context Learning in LLMs Through the Lens of Classical Supervised Learning
by: Gundem, Korel, et al.
Published: (2025)

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
by: Ding, Fei, et al.
Published: (2025)

Beyond Perplexity: A Lightweight Benchmark for Knowledge Retention in Supervised Fine-Tuning
by: Shabgahi, Soheil Zibakhsh, et al.
Published: (2026)

WikiNER-fr-gold: A Gold-Standard NER Corpus
by: Cao, Danrun, et al.
Published: (2024)

Cross-Lingual IPA Contrastive Learning for Zero-Shot NER
by: Sohn, Jimin, et al.
Published: (2025)

DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition
by: Luo, Hanjun, et al.
Published: (2024)

Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
by: Jiang, Haitao, et al.
Published: (2026)

Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning
by: Hong, Joey, et al.
Published: (2024)

You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models
by: He, Wenchong, et al.
Published: (2025)

Fine-Tuning LLMs for Low-Resource Dialect Translation: The Case of Lebanese
by: Yakhni, Silvana, et al.
Published: (2025)

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs
by: Kothari, Sara, et al.
Published: (2025)

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs
by: Nuti, Felipe, et al.
Published: (2025)

Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs
by: Pan, Xu, et al.
Published: (2025)

60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering
by: Ye, Junjie, et al.
Published: (2024)

Evaluating the Sensitivity of LLMs to Prior Context
by: Hankache, Robert, et al.
Published: (2025)

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
by: Huang, Zeyu, et al.
Published: (2025)

Prompt replay: speeding up grpo with on-policy reuse of high-signal prompts
by: Baroian, Andrei, et al.
Published: (2026)

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning
by: Khadangi, Afshin, et al.
Published: (2025)

Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
by: Ye, Junjie, et al.
Published: (2025)