Evaluation Techniques for Machine Learning Models
This article discusses six main evaluation techniques for machine learning models, including exact match, schema/constraint validation, code execution/unit testing, reference-based and rubric-based LLM judges, pairwise preference, and human evaluation.
Why it matters
Evaluating the performance of machine learning models is critical to ensure their accuracy, safety, and reliability. This article provides a comprehensive overview of the key evaluation techniques used in the industry.
Key Points
- 1There are two broad families of evaluation techniques: those that compare against a known answer, and those that use judgment
- 2Exact match, schema/constraint validation, and code execution/unit testing are techniques that compare against a known answer
- 3Reference-based LLM judge, rubric-based LLM judge, and pairwise preference are judgment-based techniques
- 4Human evaluation is the highest signal but most resource-intensive technique, used to calibrate automated judges
- 5Online monitoring is a continuous technique that scores inputs and outputs in production to route flagged interactions for human review
Details
The article provides a detailed overview of the six main evaluation techniques for machine learning models. Exact match is the simplest, checking if the output equals the known correct answer. Schema/constraint validation checks if the output conforms to the expected structure or schema. Code execution/unit testing runs the output and checks if tests pass, which is the gold standard for agents that produce code or structured plans. Reference-based LLM judge and rubric-based LLM judge use language models to score the output against a golden answer or a scoring rubric, respectively. Pairwise preference compares two outputs and selects the better one, which is useful for promotion gates. Human evaluation, while the highest signal, is too slow and expensive to run on everything, and is primarily used to calibrate the automated judges. Online monitoring continuously scores inputs and outputs in production to route flagged interactions for human review, closing the feedback loop.
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