Buchumschlag

Language Models for Text Classification: Is In-Context Learning Enough?

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Edwards, Aleksandra, Camacho-Collados, Jose
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Computation and Language Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length
von: Chen, Jingxuan, et al.
Veröffentlicht: (2026)

Efficient Text Classification with Conformal In-Context Learning
von: Pantelidis, Ippokratis, et al.
Veröffentlicht: (2025)

Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs
von: de Landa, Joseba Fernandez, et al.
Veröffentlicht: (2026)

A Dual-Directional Context-Aware Test-Time Learning for Text Classification
von: Xu, Dong, et al.
Veröffentlicht: (2025)

Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis
von: Lee, Nayeon, et al.
Veröffentlicht: (2023)

Bengali Text Classification: An Evaluation of Large Language Model Approaches
von: Hoque, Md Mahmudul, et al.
Veröffentlicht: (2026)

Reward Is Enough: LLMs Are In-Context Reinforcement Learners
von: Song, Kefan, et al.
Veröffentlicht: (2025)

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

Context-level Language Modeling by Learning Predictive Context Embeddings
von: Dai, Beiya, et al.
Veröffentlicht: (2025)

Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
von: Zhou, Yuhang, et al.
Veröffentlicht: (2023)

One Token Is Enough: Improving Diffusion Language Models with a Sink Token
von: Zhang, Zihou, et al.
Veröffentlicht: (2026)

When Is Enough Not Enough? Illusory Completion in Search Agents
von: Ko, Dayoon, et al.
Veröffentlicht: (2026)

ContextGuard: Structured Self-Auditing for Context Learning in Language Models
von: Jin, Hongbo, et al.
Veröffentlicht: (2026)

Leveraging In-Context Learning for Language Model Agents
von: Gupta, Shivanshu, et al.
Veröffentlicht: (2025)

Harnessing the Intrinsic Knowledge of Pretrained Language Models for Challenging Text Classification Settings
von: Gao, Lingyu
Veröffentlicht: (2024)

Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks
von: Guo, Yuting, et al.
Veröffentlicht: (2025)

Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysis
von: Cherukuri, Komala Subramanyam, et al.
Veröffentlicht: (2025)

In-Context Learning for Extreme Multi-Label Classification
von: D'Oosterlinck, Karel, et al.
Veröffentlicht: (2024)

Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
von: Wen, Ximing
Veröffentlicht: (2024)

Data Generation Using Large Language Models for Text Classification: An Empirical Case Study
von: Li, Yinheng, et al.
Veröffentlicht: (2024)

Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models
von: Lu, Zhenyi, et al.
Veröffentlicht: (2024)

Customizing Language Model Responses with Contrastive In-Context Learning
von: Gao, Xiang, et al.
Veröffentlicht: (2024)

Language Models Struggle to Use Representations Learned In-Context
von: Lepori, Michael A., et al.
Veröffentlicht: (2026)

Learn to Select: Exploring Label Distribution Divergence for In-Context Demonstration Selection in Text Classification
von: Jiang, Ye, et al.
Veröffentlicht: (2025)

Induction Signatures Are Not Enough: A Matched-Compute Study of Load-Bearing Structure in In-Context Learning
von: Sabry, Mohammed, et al.
Veröffentlicht: (2025)

Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets
von: Joshi, Harshit, et al.
Veröffentlicht: (2026)

Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning
von: Zhao, Hang, et al.
Veröffentlicht: (2024)

Advancing Interpretability in Text Classification through Prototype Learning
von: Wei, Bowen, et al.
Veröffentlicht: (2024)

Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks
von: Hsu, Ming-Hao, et al.
Veröffentlicht: (2023)

Training Text-to-Molecule Models with Context-Aware Tokenization
von: Kim, Seojin, et al.
Veröffentlicht: (2025)

In Context Learning and Reasoning for Symbolic Regression with Large Language Models
von: Sharlin, Samiha, et al.
Veröffentlicht: (2024)

Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
von: Park, Seong-Il, et al.
Veröffentlicht: (2024)

Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models
von: Schröder, Christopher, et al.
Veröffentlicht: (2024)

Revisiting In-Context Learning with Long Context Language Models
von: Baek, Jinheon, et al.
Veröffentlicht: (2024)

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

Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
von: Liu, Shiqi, et al.
Veröffentlicht: (2024)

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
von: Yin, Yueqin, et al.
Veröffentlicht: (2025)

L2D-Clinical: Learning to Defer for Adaptive Model Selection in Clinical Text Classification
von: Kondadadi, Rishik, et al.
Veröffentlicht: (2026)

Label Distribution Learning-Enhanced Dual-KNN for Text Classification
von: Yuan, Bo, et al.
Veröffentlicht: (2025)

Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language
von: Beliveau, Vincent, et al.
Veröffentlicht: (2024)