Query-efficient model evaluation using cached responses

2024-04-09 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

An approach for query-efficient model evaluation using cached responses, based on the Data Kernel Perspective Space (DKPS), aims to predict benchmark performance of new models with fewer queries by leveraging responses from previously evaluated models. The theoretical contribution proves DKPS-based methods are query-efficient under certain conditions. Empirically, these methods achieve comparable accuracy to baselines with substantially reduced query budgets, demonstrating a 10x reduction in queries for a given performance. The Ensemble method, which adaptively combines DKPS predictions with direct sample scores, consistently outperforms other methods across various HELM-Lite tasks (MATH, LegalBench, MedQA, WMT). The choice of embedding function significantly impacts performance at low query budgets, and an offline active query selection strategy further improves efficiency by selecting query sets that maximize goodness-of-fit on reference models.

Key takeaway

For MLOps Engineers evaluating new model variants, you should integrate DKPS-based prediction into your evaluation pipelines. This approach, particularly the Ensemble method, can reduce query costs by over 10x while maintaining accuracy. Implement offline query selection to maximize efficiency, especially at low query budgets. Carefully choose your embedding function for optimal results when using minimal queries. This strategy ensures efficient, accurate model assessment, leading to substantial cumulative savings.

Key insights

Leveraging cached model responses via Data Kernel Perspective Space enables query-efficient benchmark performance prediction.

Principles

• DKPS methods offer query-efficiency for benchmark prediction.
• Adaptive ensemble weighting improves prediction across query budgets.
• Query set selection and embedding choice significantly impact low-budget accuracy.

Method

The proposed method constructs low-dimensional DKPS representations from average embedded responses of reference models, then trains a regressor to predict benchmark scores. An ensemble combines this with direct sample scores.

In practice

• Use the Ensemble method for robust performance across query budgets.
• Implement offline query selection to optimize query sets.
• Carefully select embedding functions for low-query evaluations.

Topics

• Query Efficiency
• Model Evaluation
• Data Kernel Perspective Space
• Benchmark Prediction
• Generative Models
• HELM-Lite
• Embedding Functions

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.