:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Fukuhata, Shion, Kano, Yoshinobu
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language
Accesso online:	https://arxiv.org/abs/2504.04966
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Memorization of Named Entities in Fine-tuned BERT Models
di: Diera, Andor, et al.
Pubblicazione: (2022)

ACL: Aligned Contrastive Learning Improves BERT and Multi-exit BERT Fine-tuning
di: Li, Liz, et al.
Pubblicazione: (2026)

Lightweight Safety Guardrails Using Fine-tuned BERT Embeddings
di: Zheng, Aaron, et al.
Pubblicazione: (2024)

Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents
di: Grosjean, Juri, et al.
Pubblicazione: (2024)

Detecting Bias in Large Language Models: Fine-tuned KcBERT
di: Lee, J. K., et al.
Pubblicazione: (2024)

Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis
di: Nkhata, Gibson, et al.
Pubblicazione: (2025)

Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language
di: Saeid, Yasser, et al.
Pubblicazione: (2024)

Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams
di: Garcia, Cristiano Mesquita, et al.
Pubblicazione: (2024)

Sparse is Enough in Fine-tuning Pre-trained Large Language Models
di: Song, Weixi, et al.
Pubblicazione: (2023)

Question-Answering System for Bangla: Fine-tuning BERT-Bangla for a Closed Domain
di: Roy, Subal Chandra, et al.
Pubblicazione: (2024)

SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations
di: Solano, Jesus, et al.
Pubblicazione: (2023)

Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?
di: Hościłowicz, Jakub, et al.
Pubblicazione: (2023)

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

Correlation Dimension of Natural Language in a Statistical Manifold
di: Du, Xin, et al.
Pubblicazione: (2024)

Biomedical Entity Linking for Dutch: Fine-tuning a Self-alignment BERT Model on an Automatically Generated Wikipedia Corpus
di: Hartendorp, Fons, et al.
Pubblicazione: (2024)

Word Embedding Dimension Reduction via Weakly-Supervised Feature Selection
di: Xue, Jintang, et al.
Pubblicazione: (2024)

RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
di: Najafi, Saeed, et al.
Pubblicazione: (2024)

Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction
di: Bei, Beicheng, et al.
Pubblicazione: (2026)

Mixture of Hidden-Dimensions Transformer
di: Chen, Yilong, et al.
Pubblicazione: (2024)

KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis
di: Awlla, Kozhin muhealddin, et al.
Pubblicazione: (2025)

Mask-guided BERT for Few Shot Text Classification
di: Liao, Wenxiong, et al.
Pubblicazione: (2023)

Detecting Redundant Health Survey Questions Using Language-agnostic BERT Sentence Embedding (LaBSE)
di: Kang, Sunghoon, et al.
Pubblicazione: (2024)

Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
di: Zhang, Haode, et al.
Pubblicazione: (2023)

When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs
di: Liu, Hongcheng, et al.
Pubblicazione: (2025)

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

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

Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
di: Kumar, Nitesh, et al.
Pubblicazione: (2024)

Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
di: Zhang, Haode, et al.
Pubblicazione: (2022)

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

Energy and Carbon Considerations of Fine-Tuning BERT
di: Wang, Xiaorong, et al.
Pubblicazione: (2023)

CreditAudit: 2$^\text{nd}$ Dimension for LLM Evaluation and Selection
di: Song, Yiliang, et al.
Pubblicazione: (2026)

Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning
di: Wang, Guoli, et al.
Pubblicazione: (2026)

The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases
di: Das, Dipto, et al.
Pubblicazione: (2024)

Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection
di: Dong, Ming, et al.
Pubblicazione: (2024)

Token-level Data Selection for Safe LLM Fine-tuning
di: Li, Yanping, et al.
Pubblicazione: (2026)

Measuring Intrinsic Dimension of Token Embeddings
di: Kataiwa, Takuya, et al.
Pubblicazione: (2025)

GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning
di: Solatorio, Aivin V.
Pubblicazione: (2024)

SparseGrad: A Selective Method for Efficient Fine-tuning of MLP Layers
di: Chekalina, Viktoriia, et al.
Pubblicazione: (2024)

Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
di: Harada, Yuto, et al.
Pubblicazione: (2025)

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