@inproceedings{balkir-etal-2026-confident,
title = "Confident Rankings with Fewer Items: Adaptive {LLM} Evaluation with Continuous Scores",
author = "Balkir, Esma and
Pernthaller, Alice and
Basaldella, Marco and
Hernandez-Orallo, Jose and
Collier, Nigel",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2068/",
pages = "47092--47108",
ISBN = "979-8-89176-459-0",
abstract = "Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluation increasingly relies on generation tasks where outputs are scored continuously rather than marked correct/incorrect. We present a principled extension of IRT-based adaptive testing to continuous bounded scores (ROUGE, BLEU, LLM-as-a-Judge) by replacing the Bernoulli response distribution with a heteroskedastic normal distribution. Building on this, we introduce an uncertainty aware ranker with adaptive stopping criteria that achieves reliable model ranking while testing as few items and as cheaply as possible. We validate our method on five benchmarks spanning n-gram-based, embedding-based, and LLM-as-judge metrics. Our method improves ranking correlation by 0.13 $\tau$ over random sampling and has 99{\%} accuracy on confident predictions while using 2{\%} of the items after a one-time calibration step"
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%0 Conference Proceedings
%T Confident Rankings with Fewer Items: Adaptive LLM Evaluation with Continuous Scores
%A Balkir, Esma
%A Pernthaller, Alice
%A Basaldella, Marco
%A Hernandez-Orallo, Jose
%A Collier, Nigel
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F balkir-etal-2026-confident
%X Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluation increasingly relies on generation tasks where outputs are scored continuously rather than marked correct/incorrect. We present a principled extension of IRT-based adaptive testing to continuous bounded scores (ROUGE, BLEU, LLM-as-a-Judge) by replacing the Bernoulli response distribution with a heteroskedastic normal distribution. Building on this, we introduce an uncertainty aware ranker with adaptive stopping criteria that achieves reliable model ranking while testing as few items and as cheaply as possible. We validate our method on five benchmarks spanning n-gram-based, embedding-based, and LLM-as-judge metrics. Our method improves ranking correlation by 0.13 τ over random sampling and has 99% accuracy on confident predictions while using 2% of the items after a one-time calibration step
%U https://aclanthology.org/2026.emnlp-main.2068/
%P 47092-47108
Markdown (Informal)
[Confident Rankings with Fewer Items: Adaptive LLM Evaluation with Continuous Scores](https://aclanthology.org/2026.emnlp-main.2068/) (Balkir et al., EMNLP 2026)
ACL