BYOB: Build Your Own Benchmark

2023-09-06 · Source: Artificial Ignorance · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

The traditional AI evaluation paradigm, characterized by benchmarks like GLUE, MMLU, and SWE-bench, is becoming saturated and less useful for practical applications. These benchmarks often see models quickly achieve near-perfect scores, leading to a "treadmill of saturation" where harder versions are constantly introduced. OpenAI's audit of SWE-bench Verified, for instance, revealed 59.4% flawed test cases and widespread model contamination, where models recalled solutions rather than demonstrating true coding ability. This shift highlights a growing need for behavioral, domain-specific, and product-centric evaluations that measure how models perform in complex, open-ended environments, such as managing a simulated vending machine business (Vending-Bench), playing Diplomacy, or identifying corporate wrongdoing (SnitchBench). These new benchmarks prioritize real-world behavior over abstract intelligence scores, offering more relevant insights for product development and practical model selection.

Key takeaway

For AI Architects and Engineers selecting models for specific applications, relying solely on public leaderboard scores is increasingly unreliable. You should prioritize building custom, behavioral, and domain-specific evaluation suites that reflect your actual product's use cases and desired model behaviors. This approach ensures that your chosen models perform effectively in real-world scenarios, moving beyond abstract intelligence metrics to practical, verifiable performance.

Key insights

Traditional AI benchmarks are saturating, necessitating new evaluations focused on real-world model behavior and domain-specific performance.

Principles

• Benchmarks saturate; new ones must be harder.
• Contamination invalidates benchmark results.
• Behavioral evals reveal practical model capabilities.

Method

Develop custom evaluation suites tailored to specific product use cases or domain workflows, focusing on behavioral metrics rather than generic capability scores. Integrate these evals into an "eval-driven development" cycle.

In practice

• Use your 10 most common prompts as a personal benchmark.
• Define "good" output for each prompt.
• Compare new model outputs against saved strong/weak examples.

Topics

• AI Benchmarking
• Behavioral AI
• LLM Evaluation
• Custom Evals
• Benchmark Saturation

Code references

• petergpt/bullshit-benchmark

Best for: AI Architect, AI Engineer, NLP Engineer, Machine Learning Engineer, AI Product Manager, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Ignorance.