Running evaluations, or evals, is critical in AI product development, ensuring models align with user needs and values. These evaluations begin with clearly defined product goals, which are converted into templates for raters to follow. The results guide necessary changes to the product. A common challenge is result ambiguity, particularly due to inter-rater reliability (IRR), where disagreement among raters complicates quality judgments. Tools like Krippendorf's alpha help measure reliability, with varying thresholds depending on the seriousness of the evaluation.
Evals are crucial in AI to ensure model alignment with user needs, emphasizing human evaluations to identify gaps automation misses, like nuance and context.
Ambiguity in evaluations often arises from rater disagreement, a problem recognized in psychology. Tools exist to measure this reliability, such as Krippendorf's alpha.
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