{"total":18,"rows":[{"id":"2405.20541","title":"Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models","created":"2024-05-30","cited_by_count":84,"area":null},{"id":"2505.17799","title":"A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances","created":"2025-05-23","cited_by_count":28,"area":null},{"id":"2312.01700","title":"Data Management For Training Large Language Models: A Survey","created":"2023-12-04","cited_by_count":17,"area":null},{"id":"2403.08370","title":"SMART: Submodular Data Mixture Strategy for Instruction Tuning","created":"2024-03-13","cited_by_count":17,"area":null},{"id":"2402.14270","title":"Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization","created":"2024-02-22","cited_by_count":12,"area":null},{"id":"2408.06663","title":"Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models","created":"2024-08-13","cited_by_count":9,"area":null},{"id":"2310.16776","title":"DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection","created":"2023-10-25","cited_by_count":6,"area":null},{"id":"2407.01490","title":"LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives","created":"2024-07-01","cited_by_count":6,"area":null},{"id":"2501.02432","title":"Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding","created":"2025-01-05","cited_by_count":4,"area":null},{"id":"2511.08263","title":"ImagebindDC: Compressing Multi-modal Data with Imagebind-based Condensation","created":"2025-11-11","cited_by_count":4,"area":null},{"id":"2312.14200","title":"Efficient Architecture Search via Bi-level Data Pruning","created":"2023-12-21","cited_by_count":2,"area":null},{"id":"2406.04892","title":"Sexism Detection on a Data Diet","created":"2024-06-07","cited_by_count":2,"area":null},{"id":"2409.02725","title":"Pre-training data selection for biomedical domain adaptation using journal impact metrics","created":"2024-09-04","cited_by_count":2,"area":null},{"id":"2502.11085","title":"On the Importance of Pretraining Data Alignment for Atomic Property Prediction","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2509.23461","title":"Data-Efficient Training by Evolved Sampling","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2602.22014","title":"A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2608.30699","title":"Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2610.02593","title":"Fisher-Guided Submodular Data Selection for Continual Pre-Training of Large Language Models","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}