Latest Articles for Model Evaluation

A method combining VMD and linear models boosts forecasting accuracy.

The PoEM framework assesses language models without needing precise labels.

This study evaluates how slight changes impact language model responses.

A new method helps identify test data contamination in LLMs using token probabilities.

FSDEM offers a fresh approach to assessing feature selection techniques for data analysis.

MAPWise dataset challenges models on map-based questions and evaluates their reasoning skills.

This article discusses a new rating system for evaluating language models more fairly.

Logit Scaling enhances out-of-distribution data detection without training data.

This study evaluates machine learning models for detecting trash in rivers.

A new method for assessing robustness in ML classifiers using adversarial distance.

A closer look at how well large language models perform basic tasks.

A new method improves AI explanations through collaboration between two language models.

This research explores how topological degree assesses the effectiveness of VAEs in capturing data structure.

Study reveals how language models utilize context for accurate responses.

New methods help understand how models react to data changes.

This article examines methods for detecting data contamination in large language models.

This paper explores how bootstrap methods enhance stability and robustness in SGD models.

A new benchmark aims to improve uncertainty assessment in language models.

A new method improves model reasoning through structured programming traces.

Examining how fine-tuning affects safety in language models across various tasks.

A fresh approach to evaluating ML models using Item Response Theory for better insights.

Strong baseline models enhance the evaluation of ML systems in healthcare.

A look at confidence intervals in few-shot learning and their impact on model evaluation.

Examining the understanding and output accuracy of language models.

Research highlights using influence functions to enhance PINN performance in physics problems.

A look into effective dimension and its impact on model training.

This paper evaluates how well language models explain scientific concepts.

This article examines GAMs as a solution for predictive performance and interpretability.

Examining how hard samples affect model performance and the reliability of test accuracy.

This article examines how different layers affect LLM performance.

Soft labels can improve machine learning model performance in uncertain data scenarios.

RepairBench sets benchmarks for comparing AI models in fixing software bugs.

This method enhances the reliability of language model confidence scores.

Learn how the applicability domain affects predictive model accuracy in various fields.

A method to estimate reliability of responses from large language models.

A new method for testing language models using randomized text.

A method to improve steering vector effectiveness in language models.

Explore the impact of shortcut learning on language models and their real-world applications.

This paper examines methods to compare generative models through embedding-based representations.

A framework to balance pseudo-label learning in machine learning.