:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Rieger, Jonas, Ruckdeschel, Mattes, Wiedemann, Gregor
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	https://arxiv.org/abs/2412.04975
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine
by: Rieger, Jonas, et al.
Published: (2023)

The representation landscape of few-shot learning and fine-tuning in large language models
by: Doimo, Diego, et al.
Published: (2024)

Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
by: Razuvayevskaya, Olesya, et al.
Published: (2023)

Scalable and consistent few-shot classification of survey responses using text embeddings
by: Mjaaland, Jonas Timmann, et al.
Published: (2025)

LayerNorm: A key component in parameter-efficient fine-tuning
by: ValizadehAslani, Taha, et al.
Published: (2024)

RandLoRA: Full-rank parameter-efficient fine-tuning of large models
by: Albert, Paul, et al.
Published: (2025)

Scaling few-shot spoken word classification with generative meta-continual learning
by: Beyers, Louise, et al.
Published: (2026)

Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
by: Abdullah, Abdulhady Abas, et al.
Published: (2025)

A Human-Centric Framework for Data Attribution in Large Language Models
by: Wührl, Amelie, et al.
Published: (2026)

Birbal: An efficient 7B instruct-model fine-tuned with curated datasets
by: Jindal, Ashvini Kumar, et al.
Published: (2024)

Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification
by: Dubey, Kush
Published: (2024)

Multi-task retriever fine-tuning for domain-specific and efficient RAG
by: Béchard, Patrice, et al.
Published: (2025)

Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification
by: Chen, Huiyao, et al.
Published: (2024)

Complexity-aware fine-tuning
by: Goncharov, Andrey, et al.
Published: (2025)

LLM one-shot style transfer for Authorship Attribution and Verification
by: Miralles-González, Pablo, et al.
Published: (2025)

A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks
by: Zhang, Tianyi, et al.
Published: (2024)

Towards few-shot isolated word reading assessment
by: Smit, Reuben, et al.
Published: (2025)

Lex2Sent: A bagging approach to unsupervised sentiment analysis
by: Lange, Kai-Robin, et al.
Published: (2022)

Immunization against harmful fine-tuning attacks
by: Rosati, Domenic, et al.
Published: (2024)

Temporal fine-tuning for early risk detection
by: Thompson, Horacio, et al.
Published: (2025)

Labeling supervised fine-tuning data with the scaling law
by: Kong, Huanjun
Published: (2024)

Practical token pruning for foundation models in few-shot conversational virtual assistant systems
by: Qi, Haode, et al.
Published: (2024)

Visually grounded few-shot word learning in low-resource settings
by: Nortje, Leanne, et al.
Published: (2023)

Zeitenwenden: Detecting changes in the German political discourse
by: Lange, Kai-Robin, et al.
Published: (2024)

Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking
by: Kolli, Imene, et al.
Published: (2026)

Investigating grammatical abstraction in language models using few-shot learning of novel noun gender
by: Sukumaran, Priyanka, et al.
Published: (2024)

Multiscale attention for few‐shot image classification
by: Tong Zhou, et al.
Published: (2024)

Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
by: Zhuang, Xuanyu, et al.
Published: (2024)

Retention analysis of edited knowledge after fine-tuning
by: Wen, Fufang, et al.
Published: (2025)

Replaying pre-training data improves fine-tuning
by: Kotha, Suhas, et al.
Published: (2026)

Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
by: Su, Leilei, et al.
Published: (2023)

Understanding the effects of language-specific class imbalance in multilingual fine-tuning
by: Jung, Vincent, et al.
Published: (2024)

Improving LLM-based Ontology Matching with fine-tuning on synthetic data
by: Sousa, Guilherme, et al.
Published: (2025)

Using tournaments to calculate AUROC for zero-shot classification with LLMs
by: Yoon, WonJin, et al.
Published: (2025)

Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs
by: Zhong, Ziqian, et al.
Published: (2025)

Topic mining based on fine-tuning Sentence-BERT and LDA
by: Li, Jianheng, et al.
Published: (2025)

Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
by: Wang, Yibo, et al.
Published: (2025)

Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation
by: Tan, Shaomu, et al.
Published: (2024)

Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning
by: Dehghani, Mohammad
Published: (2024)

Data-efficient LLM Fine-tuning for Code Generation
by: Lv, Weijie, et al.
Published: (2025)