data quality

Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity Interspeech 2025 FIRE: Flexible Integration of Data Quality Ratings for Effective Pretraining EMNLP 2025 FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy ACL 2025 LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models EMNLP 2025 Meaning Variation and Data Quality in the Corpus of Founding Era American English ACL 2025 Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations EMNLP 2025 Modalities Contribute Unequally: Enhancing Medical Multi-modal Learning through Adaptive Modality Token Re-balancing ICML 2025 NeMo-Inspector: A Visualization Tool for LLM Generation Analysis NAACL 2025 Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis ICCV 2025 OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text ICLR 2025 ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study ACL 2025 Position: When Incentives Backfire, Data Stops Being Human ICML 2025 Priority on High-Quality: Selecting Instruction Data via Consistency Verification of Noise Injection EMNLP 2025 Quality Assessment of Tabular Data using Large Language Models and Code Generation EMNLP 2025 Quality Beyond A Glance: Revealing Large Quality Differences Between Web-Crawled Parallel Corpora COLING 2025 Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora ACL 2025 Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies ACL 2025 SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity EMNLP 2025 Structure Trumps Size: Rethinking Data Quality for LLM Reasoning EMNLP 2025 Tab-Shapley: Identifying Top-k Tabular Data Quality Insights AAAI 2025 The Good, the Bad, and the Debatable: A Survey on the Impacts of Data for In-Context Learning EMNLP 2025 Train a Unified Multimodal Data Quality Classifier with Synthetic Data EMNLP 2025 VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data EMNLP 2025 We Need to Measure Data Diversity in NLP — Better and Broader EMNLP 2025 A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity NAACL 2024