:: Library Catalog

Ata uhi

I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Wang, Mingyang, Adel, Heike, Lange, Lukas, Strötgen, Jannik, Schütze, Hinrich
Hōputu:	Preprint
I whakaputaina:	2024
Ngā marau:	Machine Learning Computation and Language
Urunga tuihono:	https://arxiv.org/abs/2406.18708
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Ngā tūemi rite

Rehearsal-Free Modular and Compositional Continual Learning for Language Models
mā: Wang, Mingyang, me ētahi atu.
I whakaputaina: (2024)

Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization
mā: Wang, Mingyang, me ētahi atu.
I whakaputaina: (2024)

NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis
mā: Wang, Mingyang, me ētahi atu.
I whakaputaina: (2023)

Language Mixing in Reasoning Language Models: Patterns, Impact, and Internal Causes
mā: Wang, Mingyang, me ētahi atu.
I whakaputaina: (2025)

Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion
mā: Wang, Mingyang, me ētahi atu.
I whakaputaina: (2025)

Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models
mā: Wang, Mingyang, me ētahi atu.
I whakaputaina: (2025)

Discourse-Aware In-Context Learning for Temporal Expression Normalization
mā: Gautam, Akash Kumar, me ētahi atu.
I whakaputaina: (2024)

GLUScope: A Tool for Analyzing GLU Neurons in Transformer Language Models
mā: Gerstner, Sebastian, me ētahi atu.
I whakaputaina: (2026)

Understanding Gated Neurons in Transformers from Their Input-Output Functionality
mā: Gerstner, Sebastian, me ētahi atu.
I whakaputaina: (2025)

Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall
mā: Wang, Qianli, me ētahi atu.
I whakaputaina: (2025)

Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding
mā: Feng, Qi, me ētahi atu.
I whakaputaina: (2025)

HYPEROFA: Expanding LLM Vocabulary to New Languages via Hypernetwork-Based Embedding Initialization
mā: Özeren, Enes, me ētahi atu.
I whakaputaina: (2025)

ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning
mā: Liu, Yongkang, me ētahi atu.
I whakaputaina: (2026)

BlackboxNLP-2025 MIB Shared Task: Exploring Ensemble Strategies for Circuit Localization Methods
mā: Mondorf, Philipp, me ētahi atu.
I whakaputaina: (2025)

SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
mā: Liu, Yongkang, me ētahi atu.
I whakaputaina: (2026)

CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation
mā: Ziegler, Ingo, me ētahi atu.
I whakaputaina: (2024)

LongForm: Effective Instruction Tuning with Reverse Instructions
mā: Köksal, Abdullatif, me ētahi atu.
I whakaputaina: (2023)

OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining
mā: Liu, Yihong, me ētahi atu.
I whakaputaina: (2023)

The Anatomy of an Edit: Mechanism-Guided Activation Steering for Knowledge Editing
mā: Cao, Yuan, me ētahi atu.
I whakaputaina: (2026)

Derivational Morphology Reveals Analogical Generalization in Large Language Models
mā: Hofmann, Valentin, me ētahi atu.
I whakaputaina: (2024)

In-Context Learning Learns Label Relationships but Is Not Conventional Learning
mā: Kossen, Jannik, me ētahi atu.
I whakaputaina: (2023)

HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
mā: Liu, Yongkang, me ētahi atu.
I whakaputaina: (2024)

Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification
mā: Pätz, Lukas, me ētahi atu.
I whakaputaina: (2025)

MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
mā: Köksal, Abdullatif, me ētahi atu.
I whakaputaina: (2024)

Steering MoE LLMs via Expert (De)Activation
mā: Fayyaz, Mohsen, me ētahi atu.
I whakaputaina: (2025)

On the Entity-Level Alignment in Crosslingual Consistency
mā: Liu, Yihong, me ētahi atu.
I whakaputaina: (2025)

Thought Flow Nets: From Single Predictions to Trains of Model Thought
mā: Schuff, Hendrik, me ētahi atu.
I whakaputaina: (2021)

TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech
mā: Venturini, Shamira, me ētahi atu.
I whakaputaina: (2026)

BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning
mā: Nie, Ercong, me ētahi atu.
I whakaputaina: (2024)

Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning
mā: Bi, Jiaxi, me ētahi atu.
I whakaputaina: (2026)

Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models
mā: Araujo, Vladimir, me ētahi atu.
I whakaputaina: (2024)

Refusal Direction is Universal Across Safety-Aligned Languages
mā: Wang, Xinpeng, me ētahi atu.
I whakaputaina: (2025)

Mitigating Copy Bias in In-Context Learning through Neuron Pruning
mā: Ali, Ameen, me ētahi atu.
I whakaputaina: (2024)

LangSAMP: Language-Script Aware Multilingual Pretraining
mā: Liu, Yihong, me ētahi atu.
I whakaputaina: (2024)

Language Model-Driven Data Pruning Enables Efficient Active Learning
mā: Azeemi, Abdul Hameed, me ētahi atu.
I whakaputaina: (2024)

XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
mā: Lin, Peiqin, me ētahi atu.
I whakaputaina: (2024)

Learning to Learn for Few-shot Continual Active Learning
mā: Ho, Stella, me ētahi atu.
I whakaputaina: (2023)

Towards Compositionality in Concept Learning
mā: Stein, Adam, me ētahi atu.
I whakaputaina: (2024)

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation
mā: Yang, Wenjie, me ētahi atu.
I whakaputaina: (2025)

COPAL: Continual Pruning in Large Language Generative Models
mā: Malla, Srikanth, me ētahi atu.
I whakaputaina: (2024)