Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark Evaluation
Yotam Perlitz, Ariel Gera, Ofir Arviv, Asaf Yehudai, Elron Bandel, Eyal Shnarch, Michal Shmueli-Scheuer, Leshem Choshen
2024-07-19
Summary
This paper discusses the importance of properly testing benchmarks used to evaluate language models (LLMs). It introduces a method called Benchmark Agreement Testing (BAT) and provides guidelines to ensure that these tests are reliable and meaningful.
What's the problem?
As many new benchmarks are created to assess LLMs, it becomes crucial to verify whether these benchmarks accurately measure what they claim to. However, there are currently no standardized procedures for testing the agreement between new benchmarks and established ones. This lack of standardization can lead to incorrect conclusions, causing mistrust in the benchmarks and making it difficult for researchers to choose the right one for their needs.
What's the solution?
The authors analyzed over 40 existing benchmarks and identified several important methodological choices that can affect the results of BAT. They proposed a set of best practices for conducting BAT to improve its reliability. Additionally, they introduced BenchBench, a Python package designed to facilitate BAT, along with a leaderboard that evaluates benchmarks against each other. These tools aim to standardize the testing process and enhance the validity of benchmark evaluations.
Why it matters?
This research is significant because it helps ensure that benchmarks used in language model research are trustworthy and effective. By establishing standardized testing procedures, researchers can make better-informed decisions about which benchmarks to use, ultimately leading to more reliable advancements in the field of natural language processing.
Abstract
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