Model Evaluation

Systematic approaches to measuring and assessing the capabilities, properties, and limitations of language models. Model evaluation encompasses automated metrics (e.g., accuracy on benchmarks, toxicity detection), human evaluation frameworks (e.g., A/B testing, head-to-head comparisons), and task-specific assessment techniques.

For alignment purposes, evaluation frameworks measure properties like helpfulness, honesty, harmlessness, and consistency with human preferences. Evaluation is critical both for research (understanding which training techniques work) and for deployment (assessing safety and capability).

Key papers

• TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP — Framework for standardized evaluation of adversarial robustness across 16 attacks, 82+ pre-trained models, and multiple NLP tasks; enables fair benchmarking and reproducible assessment of model vulnerabilities.
• Askell et al. (2021) — Introduces interactive evaluation framework for alignment using human feedback; defines helpfulness, honesty, harmlessness (HHH) criteria

Related topics

• AI Alignment (primary use case for model evaluation)
• Language Models (what is being evaluated)
• Reinforcement Learning from Human Feedback (often evaluated to measure alignment effectiveness)