:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Oguntade, Abass
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Machine Learning
Online Access:	https://arxiv.org/abs/2603.23534
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing
by: Kim, JongWoo, et al.
Published: (2024)

Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification
by: Kuo, Hsun-Yu, et al.
Published: (2024)

Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models
by: Kaddour, Jean, et al.
Published: (2023)

Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
by: Srinivasan, Krishna Prasad Varadarajan, et al.
Published: (2024)

Medical Concept Normalization in a Low-Resource Setting
by: Patzelt, Tim
Published: (2024)

Mortgage Language Model: Domain-Adaptive Pretraining with Residual Instruction, Alignment Tuning, and Task-Specific Routing
by: Jain, Manish, et al.
Published: (2025)

Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models
by: Sedykh, Ivan, et al.
Published: (2026)

RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
by: Han, Wei, et al.
Published: (2026)

Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
by: Zhou, Yue, et al.
Published: (2024)

GRPO++: Enhancing Dermatological Reasoning under Low Resource Settings
by: Swapnil, Ismam Nur, et al.
Published: (2025)

Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP
by: Visser, Ruan, et al.
Published: (2026)

Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts
by: Somayajula, Sai Ashish, et al.
Published: (2024)

Joint Localization and Activation Editing for Low-Resource Fine-Tuning
by: Lai, Wen, et al.
Published: (2025)

Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance
by: Wang, Yao, et al.
Published: (2025)

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
by: Lu, Xudong, et al.
Published: (2024)

Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning
by: Khade, Omkar, et al.
Published: (2024)

LoQT: Low-Rank Adapters for Quantized Pretraining
by: Loeschcke, Sebastian, et al.
Published: (2024)

CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data
by: Ehab, Mohamed, et al.
Published: (2026)

Not All LLM Reasoners Are Created Equal
by: Hosseini, Arian, et al.
Published: (2024)

Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks
by: Seo, Minju, et al.
Published: (2024)

Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings
by: Arora, Aashish, et al.
Published: (2024)

Language Models Improve When Pretraining Data Matches Target Tasks
by: Mizrahi, David, et al.
Published: (2025)

Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling
by: Grangier, David, et al.
Published: (2024)

How Does Code Pretraining Affect Language Model Task Performance?
by: Petty, Jackson, et al.
Published: (2024)

APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
by: Zhao, Bowen, et al.
Published: (2024)

Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection
by: Kang, Jiawen, et al.
Published: (2024)

Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs
by: Dong, Jiancheng, et al.
Published: (2024)

Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models
by: Kunstner, Frederik, et al.
Published: (2024)

Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
by: Ju, Yiming, et al.
Published: (2024)

Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control
by: Zhang, Jiazheng, et al.
Published: (2026)

Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
by: Riabi, Arij, et al.
Published: (2021)

Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization
by: Mukherjee, Subhojyoti, et al.
Published: (2025)

Weight-Inherited Distillation for Task-Agnostic BERT Compression
by: Wu, Taiqiang, et al.
Published: (2023)

Train Once, Answer All: Many Pretraining Experiments for the Cost of One
by: Bordt, Sebastian, et al.
Published: (2025)

SOI Matters: Analyzing Multi-Setting Training Dynamics in Pretrained Language Models via Subsets of Interest
by: Vassef, Shayan, et al.
Published: (2025)

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
by: Zhang, Wenqi, et al.
Published: (2025)

MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
by: Köksal, Abdullatif, et al.
Published: (2024)

Vision-Language Models Create Cross-Modal Task Representations
by: Luo, Grace, et al.
Published: (2024)

Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
by: Baumgart, Gustav A., et al.
Published: (2024)

Fine Tuning Methods for Low-resource Languages
by: Bakkenes, Tim, et al.
Published: (2025)