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Data Augmentation

3622 directly classified papers

Papers per year

Papers

TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation EMNLP 2025
Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts EMNLP 2025
SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity EMNLP 2025
Linguistic Alignment Predicts Learning in Small Group Tutoring Sessions EMNLP 2025
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation EMNLP 2025
Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs EMNLP 2025
Rethinking Data Selection at Scale: Random Selection is Almost All You Need EMNLP 2025
Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL EMNLP 2025
Enhancing Hate Speech Classifiers through a Gradient-assisted Counterfactual Text Generation Strategy EMNLP 2025
Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities EMNLP 2025
Moral Framing in Politics (MFiP): A new resource and models for moral framing EMNLP 2025
GReX: A Graph Neural Network-Based Rerank-then-Expand Method for Detecting Conflicts Among Legal Articles in Korean Criminal Law EMNLP 2025
Enhancing Large Vision-Language Models with Ultra-Detailed Image Caption Generation EMNLP 2025
Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text EMNLP 2025
Machine-generated text detection prevents language model collapse EMNLP 2025
GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection EMNLP 2025
LLM-Driven Completeness and Consistency Evaluation for Cultural Heritage Data Augmentation in Cross-Modal Retrieval EMNLP 2025
VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data EMNLP 2025
Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance EMNLP 2025
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning EMNLP 2025
Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining EMNLP 2025
Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs EMNLP 2025
Recontextualizing Revitalization: A Mixed Media Approach to Reviving the Nüshu Language EMNLP 2025
SynC-LLM: Generation of Large-Scale Synthetic Circuit Code with Hierarchical Language Models EMNLP 2025
Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval EMNLP 2025