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arxiv:2610.03825

Same Output, Different Gold: Measuring How Reference Choice Moves a Multilingual Benchmark Score

Published on Oct 2
ยท Submitted by
Parth Kulshreshtha
on Oct 13
Authors:
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Abstract

A benchmark score compares a system output against a reference, and methodological attention falls almost entirely on the first term. We measure the second. The retained annotation record of a six-language benchmark for personally identifiable information contains two independent annotator labellings, the aggregate shipped as gold, and a reviewer gold from independent expert re-annotation of a sample. Using it, we hold the scored output fixed and exchange the reference. The score moves by 4.95 F1 points for one output and 2.00 for the other (95% CIs [3.23, 6.27] and [0.26, 3.37]), and by 7.55 in the worst language. The comparison between two outputs moves as well: the paired interaction between reference and system is +2.95 points (CI [+1.97, +3.87]), survives correction for multiple testing, and changes one language's margin outright. The cause is an undocumented aggregation default that usually kept one annotator when adjudication did not fire, making the shipped reference a partial copy of an output being scored. We report the resulting reference-sensitivity band, show how to compute one from any retained annotation record, and argue that the quantity belongs beside the score.

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Benchmark papers usually treat the gold labels as fixed and focus on the system output. We hold the output fixed and swap the reference instead. Using the retained annotation record of a six-language PII benchmark, changing which label set counts as gold moves F1 by up to 4.95 points overall and 7.55 in the worst language. It also changes the gap between two systems (paired interaction +2.95, CI [+1.97, +3.87]). The cause was an undocumented aggregation default that made the shipped gold a partial copy of one system being scored. We show how to compute this "reference-sensitivity band" from any retained annotation record and argue it should be reported next to the score.

Poster at the NeurIPS 2026 TAE workshop. Question for the community: does your benchmark keep its raw annotator labels, and would you report a band like this?

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