model performance

Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies ACL 2025 Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check EMNLP 2025 TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments ICML 2025 Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests EMNLP 2025 Understanding LLM Development Through Longitudinal Study: Insights from the Open Ko-LLM Leaderboard NAACL 2025 When to retrain a machine learning model ICML 2025 "A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation" ECCV 2024 DsDm: Model-Aware Dataset Selection with Datamodels ICML 2024 Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic ICML 2024 How Good is a Single Basin? AISTATS 2024 Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance ACL 2024 Machine Unlearning via Null Space Calibration IJCAI 2024 NLEBench+NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in Norwegian EMNLP 2024 On-Demand Federated Learning for Arbitrary Target Class Distributions AISTATS 2024 On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective AISTATS 2024 Post-hoc Part-Prototype Networks ICML 2024 Reasons and Solutions for the Decline in Model Performance after Editing NeurIPS 2024 Rethinking Tabular Data Understanding with Large Language Models NAACL 2024 Revisiting the Impact of Pursuing Modularity for Code Generation EMNLP 2024 The Data Addition Dilemma MLHC 2024 Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance ACL 2024 Vygotsky Distance: Measure for Benchmark Task Similarity COLING 2024 Annotation Sensitivity: Training Data Collection Methods Affect Model Performance EMNLP 2023 Are Emergent Abilities of Large Language Models a Mirage? NeurIPS 2023 CAME: Contrastive Automated Model Evaluation ICCV 2023