@TheAhmadOsman: INCREDIBLE The MOST COMPLETE GUIDE for understanding benchmarks and evals, and why training on them is intentionally mi…
Summary
A comprehensive free online guide covering benchmarks, evaluation, contamination, and proper practices for machine learning and LLMs is now available, emphasizing the importance of clean measurement and avoiding misleading training on test sets.
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Cached at: 06/11/26, 09:42 PM
INCREDIBLE
The MOST COMPLETE GUIDE for understanding benchmarks and evals, and why training on them is intentionally misleading is now available online to read for free
Covers the fundamentals
- What machine learning is actually trying to measure (generalization vs memorization)
- Data roles and why splits must stay sacred
- Leakage types and benchmark contamination
- Why LLMs make contamination uniquely hard (web-scale + synthetic + discussion + agents)
- The full contamination pipeline and semantic duplicates
- A practical taxonomy of “training on the test set”
- Why public benchmarks age, saturate, and stop working
Then the practical standards for clean measurement
- Proper evaluation design for classical ML and for LLMs
- Protocol freezing, exclusion lists, and honest reporting
- The rigorous before/during/after hygiene checklist
- The 2026 standard for serious LLM evaluation
- Benchmark lifecycle management and public goods thinking
- What is not a cardinal sin and what is INTENTIONALLY MISLEADING
You should read this, and if you cannot now then you most definitely wanna bookmark it for later
The benchmarks / evals / test sets are the rulers. Don’t bend them.
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