{"total":32,"rows":[{"id":"2407.01492","title":"RegMix: Data Mixture as Regression for Language Model Pre-training","created":"2024-07-01","cited_by_count":177,"area":null},{"id":"2406.06046","title":"MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models","created":"2024-06-10","cited_by_count":100,"area":null},{"id":"2409.06857","title":"What is the Role of Small Models in the LLM Era: A Survey","created":"2024-09-10","cited_by_count":41,"area":null},{"id":"2410.07064","title":"Data Selection via Optimal Control for Language Models","created":"2024-10-09","cited_by_count":38,"area":null},{"id":"2406.19976","title":"ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting","created":"2024-06-28","cited_by_count":37,"area":null},{"id":"2502.06733","title":"Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining","created":"2025-02-10","cited_by_count":16,"area":null},{"id":"2505.23811","title":"LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions","created":"2025-05-27","cited_by_count":12,"area":null},{"id":"2405.03869","title":"Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models","created":"2024-05-06","cited_by_count":10,"area":null},{"id":"2505.18513","title":"Enhancing Training Data Attribution with Representational Optimization","created":"2025-05-24","cited_by_count":8,"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":"2511.04715","title":"First is Not Really Better Than Last: Evaluating Layer Choice and Aggregation Strategies in Language Model Data Influence Estimation","created":"2025-11-06","cited_by_count":5,"area":null},{"id":"2603.26164","title":"DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models","created":"2026-03-27","cited_by_count":5,"area":null},{"id":"2502.05551","title":"FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy","created":"2025-02-08","cited_by_count":4,"area":null},{"id":"2507.03648","title":"Disentangling the Roles of Representation and Selection in Data Pruning","created":"2025-07-04","cited_by_count":4,"area":null},{"id":"2501.01473","title":"Unraveling Indirect In-Context Learning Using Influence Functions","created":"2025-01-01","cited_by_count":3,"area":null},{"id":"2501.06708","title":"Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights","created":"2025-01-12","cited_by_count":3,"area":null},{"id":"2504.00695","title":"ToReMi: Topic-Aware Data Reweighting for Dynamic Pre-Training Data Selection","created":"2025-04-01","cited_by_count":3,"area":null},{"id":"2507.09758","title":"Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding","created":"2025-07-13","cited_by_count":3,"area":null},{"id":"2411.03287","title":"The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare","created":"2024-11-05","cited_by_count":2,"area":null},{"id":"2505.14070","title":"Enhancing LLMs via High-Knowledge Data Selection","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2403.11103","title":"ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models","created":"2024-03-17","cited_by_count":1,"area":null},{"id":"2408.14763","title":"Channel Matters: Estimating Channel Influence for Multivariate Time Series","created":"2024-08-27","cited_by_count":1,"area":null},{"id":"2412.08147","title":"Variational Model Merging for Pareto Front Estimation in Multitask Finetuning","created":"2024-12-11","cited_by_count":1,"area":null},{"id":"2501.14113","title":"RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity","created":"2025-01-23","cited_by_count":1,"area":null},{"id":"2502.00761","title":"FIRE: Flexible Integration of Data Quality Ratings for Effective Pre-Training","created":"2025-02-02","cited_by_count":1,"area":null},{"id":"2412.07210","title":"EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models","created":"2024-12-10","cited_by_count":0,"area":null},{"id":"2509.15556","title":"Exploring Polyglot Harmony: On Multilingual Data Allocation for Large Language Models Pretraining","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2510.03950","title":"What Is The Performance Ceiling of My Classifier? Utilizing Category-Wise Influence Functions for Pareto Frontier Analysis","created":"2025-10-04","cited_by_count":0,"area":null},{"id":"2511.09901","title":"Explore and Establish Synergistic Effects Between Weight Pruning and Coreset Selection in Neural Network Training","created":"2025-11-13","cited_by_count":0,"area":null},{"id":"2606.23591","title":"Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2610.00436","title":"Every Batch Is Its Own Validation Set: Leave-One-Out Gradient Matching for Online Data Selection in LLM Fine-Tuning","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.01674","title":"Invent a Dataset: Measuring dataset generation abilities with zero seed","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}