downstream performance

Comparative Analysis of the Intrinsic Metrics for Tokenizers and their effect on Downstream Tasks for Hindi and Marathi ACL 2026 Hierarchical Dataset Selection for High-Quality Data Sharing AAAI 2026 RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Sets EACL 2026 Beyond Text Compression: Evaluating Tokenizers Across Scales ACL 2025 Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models ICML 2025 Generate but Verify: Answering with Faithfulness in RAG-based Question Answering IJCNLP 2025 How to Evaluate Reward Models for RLHF ICLR 2025 Instability in Downstream Task Performance During LLM Pretraining EMNLP 2025 Intrinsic Bias is Predicted by Pretraining Data and Correlates with Downstream Performance in Vision-Language Encoders NAACL 2025 Latent Action Learning Requires Supervision in the Presence of Distractors ICML 2025 LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws ICML 2025 Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations EMNLP 2025 Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions EMNLP 2025 PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs ICLR 2025 Predictive Data Selection: The Data That Predicts Is the Data That Teaches ICML 2025 Projection Head is Secretly an Information Bottleneck ICLR 2025 Scaling Laws for Downstream Task Performance in Machine Translation ICLR 2025 Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on Text ACL 2025 Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning ICML 2025 Tokenization is Sensitive to Language Variation ACL 2025 Towards Early Prediction of Self-Supervised Speech Model Performance Interspeech 2025 Accelerating Augmentation Invariance Pretraining NeurIPS 2024 Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation EMNLP 2024 CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT MLHC 2024 D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models NeurIPS 2024