Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark
  Evaluation
Recent advancements in Language Models (LMs) have catalyzed the creation of
multiple benchmarks, designed to assess these models' general capabilities. A
crucial task, however, is assessing the validity of the benchmarks themselves.
This is most commonly done via Benchmark Agreement Testing (BAT), where new
benchmarks are validated against established ones using some agreement metric
(e.g., rank correlation). Despite the crucial role of BAT for benchmark
builders and consumers, there are no standardized procedures for such agreement
testing. This deficiency can lead to invalid conclusions, fostering mistrust in
benchmarks and upending the ability to properly choose the appropriate
benchmark to use. By analyzing over 40 prominent benchmarks, we demonstrate how
some overlooked methodological choices can significantly influence BAT results,
potentially undermining the validity of conclusions. To address these
inconsistencies, we propose a set of best practices for BAT and demonstrate how
utilizing these methodologies greatly improves BAT robustness and validity. To
foster adoption and facilitate future research,, we introduce BenchBench, a
python package for BAT, and release the BenchBench-leaderboard, a
meta-benchmark designed to evaluate benchmarks using their peers. Our findings
underscore the necessity for standardized BAT, ensuring the robustness and
validity of benchmark evaluations in the evolving landscape of language model
research.
  BenchBench Package: https://github.com/IBM/BenchBench
  Leaderboard: https://huggingface.co/spaces/per/BenchBench
6699ed99004e418e714b8fbf
Yotam Perlitz
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2024-07-23T07:48:20.236Z
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/avatars/307aac0c6cfe1879f6a5e5e1d6c10413.svg
Yotam Perlitz
user
per
6699ed99004e418e714b8fc0
Ariel Gera
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2024-12-13T13:59:22.592Z
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Ariel Gera
user
arielgera
6699ed99004e418e714b8fc1
Ofir Arviv
6699ed99004e418e714b8fc2
Asaf Yehudai
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2024-12-16T09:43:18.742Z
638324f862badff43269e588
/avatars/907a39a9b44fc8b7f3fad35858b01fb7.svg
Asaf Yehudai
user
Asaf-Yehudai
6699ed99004e418e714b8fc3
Elron Bandel
6699ed99004e418e714b8fc4
Eyal Shnarch
6699ed99004e418e714b8fc5
Michal Shmueli-Scheuer
6699ed99004e418e714b8fc6
Leshem Choshen
claimed_verified
2024-12-06T15:58:07.984Z
61bf40824b4300d0fb0acf59
https://cdn-avatars.huggingface.co/v1/production/uploads/1644224872623-61bf40824b4300d0fb0acf59.jpeg
Leshem Choshen
user
borgr
Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark
  Evaluation
Recent advancements in Language Models (LMs) have catalyzed the creation of
multiple benchmarks, designed to assess these models' general capabilities. A
crucial task, however, is assessing the validity of the benchmarks themselves.
This is most commonly done via Benchmark Agreement Testing (BAT), where new
benchmarks are validated against established ones using some agreement metric
(e.g., rank correlation). Despite the crucial role of BAT for benchmark
builders and consumers, there are no standardized procedures for such agreement
testing. This deficiency can lead to invalid conclusions, fostering mistrust in
benchmarks and upending the ability to properly choose the appropriate
benchmark to use. By analyzing over 40 prominent benchmarks, we demonstrate how
some overlooked methodological choices can significantly influence BAT results,
potentially undermining the validity of conclusions. To address these
inconsistencies, we propose a set of best practices for BAT and demonstrate how
utilizing these methodologies greatly improves BAT robustness and validity. To
foster adoption and facilitate future research,, we introduce BenchBench, a
python package for BAT, and release the BenchBench-leaderboard, a
meta-benchmark designed to evaluate benchmarks using their peers. Our findings
underscore the necessity for standardized BAT, ensuring the robustness and
validity of benchmark evaluations in the evolving landscape of language model
research.
  BenchBench Package: https://github.com/IBM/BenchBench
  Leaderboard: https://huggingface.co/spaces/per/BenchBench
Analysis of Benchmark Agreement Testing methods reveals the need for standardized procedures to ensure the validity and robustness of language model benchmark evaluations.
Benchmark Agreement Testing BAT agreement metric rank correlation BenchBench BenchBench-leaderboard meta-benchmark