Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks

Overview

The paper presents ATLAS, an adaptive testing framework for evaluating large language models (LLMs) using Item Response Theory (IRT). This method addresses the inefficiencies of static benchmarks by significantly reducing the number of items needed for accurate evaluation.

Key Insights

• Adaptive Testing Efficiency: ATLAS reduces evaluation items by 90% while maintaining precision, using only 42 items instead of 5,608 on the HellaSwag benchmark, achieving a 0.154 Mean Absolute Error (MAE).
• Item Quality Issues: Analysis of five major benchmarks shows 3-6% of items have negative discrimination, indicating annotation errors.
• Model Ranking Variability: IRT-based rankings differ from accuracy-based ones, with 23-31% of models shifting by more than 10 rank positions.

BFSI Relevance

• Why Relevant: Efficient and accurate model evaluation is crucial for financial services relying on AI for decision-making.
• Primary Sector: Financial Services
• Subsectors: Asset Management, Risk Management
• Actionable Implications: BFSI professionals should consider adaptive testing frameworks like ATLAS for AI model evaluation to ensure accuracy and efficiency in decision-making processes.