Image de couverture de livre

A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Pecher, Branislav, Srba, Ivan, Bielikova, Maria
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Artificial Intelligence Computation and Language
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices
par: Pecher, Branislav, et autres
Publié: (2024)

Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
par: Pecher, Branislav, et autres
Publié: (2024)

Automatic Combination of Sample Selection Strategies for Few-Shot Learning
par: Pecher, Branislav, et autres
Publié: (2024)

Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification
par: Pecher, Branislav, et autres
Publié: (2026)

Revisiting Prompt Sensitivity in Large Language Models for Text Classification: The Role of Prompt Underspecification
par: Pecher, Branislav, et autres
Publié: (2026)

PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark
par: Belanec, Robert, et autres
Publié: (2025)

Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation
par: Pecher, Branislav, et autres
Publié: (2024)

KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection
par: Hromadka, Timo, et autres
Publié: (2023)

Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification
par: Cegin, Jan, et autres
Publié: (2024)

Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation
par: Cegin, Jan, et autres
Publié: (2024)

OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
par: Toshniwal, Shubham, et autres
Publié: (2024)

RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets
par: Cegin, Jan, et autres
Publié: (2025)

Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide
par: Szep, Marton, et autres
Publié: (2024)

LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models
par: Kostikova, Aida, et autres
Publié: (2025)

Leveraging Large Language Models for Suicide Detection on Social Media with Limited Labels
par: Nguyen, Vy, et autres
Publié: (2024)

In-Context Learning Learns Label Relationships but Is Not Conventional Learning
par: Kossen, Jannik, et autres
Publié: (2023)

Finding Culture-Sensitive Neurons in Vision-Language Models
par: Zhao, Xiutian, et autres
Publié: (2025)

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
par: Havrilla, Alex, et autres
Publié: (2024)

When Domains Interact: Asymmetric and Order-Sensitive Cross-Domain Effects in Reinforcement Learning for Reasoning
par: Yang, Wang, et autres
Publié: (2026)

A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
par: Laskar, Md Tahmid Rahman, et autres
Publié: (2024)

On Evaluation Protocols for Data Augmentation in a Limited Data Scenario
par: Piedboeuf, Frédéric, et autres
Publié: (2024)

Guided Perturbation Sensitivity (GPS): Detecting Adversarial Text via Embedding Stability and Word Importance
par: Tuck, Bryan E., et autres
Publié: (2025)

Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
par: Yu, Simon, et autres
Publié: (2023)

Learning and Enforcing Context-Sensitive Control for LLMs
par: Albinhassan, Mohammad, et autres
Publié: (2026)

On the Relation between Sensitivity and Accuracy in In-context Learning
par: Chen, Yanda, et autres
Publié: (2022)

From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning
par: Alcoforado, Alexandre, et autres
Publié: (2024)

Self-Supervised Multimodal Learning: A Survey
par: Zong, Yongshuo, et autres
Publié: (2023)

Reinforcement Learning Enhanced LLMs: A Survey
par: Wang, Shuhe, et autres
Publié: (2024)

The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators
par: Huang, Tzu-Heng, et autres
Publié: (2024)

Synthetic vs. Gold: The Role of LLM Generated Labels and Data in Cyberbullying Detection
par: Kazemi, Arefeh, et autres
Publié: (2025)

Transformers Learn Low Sensitivity Functions: Investigations and Implications
par: Vasudeva, Bhavya, et autres
Publié: (2024)

Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
par: Bellamy, David R., et autres
Publié: (2023)

A Survey of Reinforcement Learning for Large Reasoning Models
par: Zhang, Kaiyan, et autres
Publié: (2025)

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
par: Li, Xunkai, et autres
Publié: (2024)

An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
par: Pavlovic, Maja, et autres
Publié: (2026)

PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models
par: Belanec, Robert, et autres
Publié: (2025)

In-Context Learning Dynamics with Random Binary Sequences
par: Bigelow, Eric J., et autres
Publié: (2023)

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications
par: Tong, Ziyi, et autres
Publié: (2026)

Limits of Transformer Language Models on Learning to Compose Algorithms
par: Thomm, Jonathan, et autres
Publié: (2024)

Continual Learning of Large Language Models: A Comprehensive Survey
par: Shi, Haizhou, et autres
Publié: (2024)