# Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench

arXiv: https://arxiv.org/abs/2407.13696 · HTML: https://mlsanity.com/paper/2407.13696/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [State of What Art? A Call for Multi-Prompt LLM Evaluation](https://mlsanity.com/paper/2401.00595/) (2023)
- [tinyBenchmarks: evaluating LLMs with fewer examples](https://mlsanity.com/paper/2402.14992/) (2024)
- [Anchor Points: Benchmarking Models with Much Fewer Examples](https://mlsanity.com/paper/2309.08638/) (2023)
- [Prompt2Model: Generating Deployable Models from Natural Language Instructions](https://mlsanity.com/paper/2308.12261/) (2023)
- [Holmes: A Benchmark to Assess the Linguistic Competence of Language Models](https://mlsanity.com/paper/2404.18923/) (2024)
- [UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs](https://mlsanity.com/paper/2404.07584/) (2024)
- [Do Question Answering Modeling Improvements Hold Across Benchmarks?](https://mlsanity.com/paper/2102.01065/) (2021)
- [Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI](https://mlsanity.com/paper/2401.14019/) (2024)
- [Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition](https://mlsanity.com/paper/2404.08008/) (2024)
- [TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning](https://mlsanity.com/paper/2402.13125/) (2024)
- [The Validity of Evaluation Results: Assessing Concurrence Across Compositionality Benchmarks](https://mlsanity.com/paper/2310.17514/) (2023)
- [Efficient Lifelong Model Evaluation in an Era of Rapid Progress](https://mlsanity.com/paper/2402.19472/) (2024)

## Built on by (most cited first, 27 of 27)

- [Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory](https://mlsanity.com/paper/2505.15055/) (2025)
- [Correlated Errors in Large Language Models](https://mlsanity.com/paper/2506.07962/) (2025)
- [Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking](https://mlsanity.com/paper/2409.15268/) (2024)
- [Fluid Language Model Benchmarking](https://mlsanity.com/paper/2509.11106/) (2025)
- [GameArena: Evaluating LLM Reasoning through Live Computer Games](https://mlsanity.com/paper/2412.06394/) (2024)
- [JuStRank: Benchmarking LLM Judges for System Ranking](https://mlsanity.com/paper/2412.09569/) (2024)
- [Eureka: Evaluating and Understanding Large Foundation Models](https://mlsanity.com/paper/2409.10566/) (2024)
- [MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation](https://mlsanity.com/paper/2505.17123/) (2025)
- [WildIFEval: Instruction Following in the Wild](https://mlsanity.com/paper/2503.06573/) (2025)
- [BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks](https://mlsanity.com/paper/2410.12974/) (2024)
- [Arena-Lite: Efficient and Reliable Large Language Model Evaluation via Tournament-Based Direct Comparisons](https://mlsanity.com/paper/2411.01281/) (2024)
- [Toward a unified framework for data-efficient evaluation of large language models](https://mlsanity.com/paper/2510.04051/) (2025)
- [BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks](https://mlsanity.com/paper/2602.06221/) (2026)
- [BenchBench: Benchmarking Automated Benchmark Generation](https://mlsanity.com/paper/2603.20807/) (2026)
- [Submodular Benchmark Selection](https://mlsanity.com/paper/2605.02209/) (2026)
- [Are Bias Evaluation Methods Biased ?](https://mlsanity.com/paper/2506.17111/) (2025)
- [When LLMs Benchmark Themselves: Deconstructing Self-Bias in Automated Evaluation](https://mlsanity.com/paper/2509.26600/) (2025)
- [Mapping Overlaps in Benchmarks through Perplexity in the Wild](https://mlsanity.com/paper/2509.23488/) (2025)
- [Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks](https://mlsanity.com/paper/2607.28685/) (2026)
- [Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation](https://mlsanity.com/paper/2609.04298/) (2026)
- [Feedback Indices to Evaluate LLM Responses to Rebuttals for Multiple Choice Type Questions](https://mlsanity.com/paper/2601.03285/) (2026)
- [Growing Pains: Extensible and Efficient LLM Benchmarking Via Fixed Parameter Calibration](https://mlsanity.com/paper/2604.12843/) (2026)
- [Correct Looks Better: Pairwise Comparisons Reveal Accuracy Rankings](https://mlsanity.com/paper/2606.09409/) (2026)
- [SidConArena: An Environment Evaluating Agents in Open-Ended,Positive-Sum Bargaining Game](https://mlsanity.com/paper/2606.27397/) (2026)
- [Frontier AI Forecasting Has a Measurement Problem: An Audit of Progress Evidence](https://mlsanity.com/paper/2608.14903/) (2026)
- [Balance of Benchmarks: Semantic Density Reweighting for Task-Conditioned Model Comparison](https://mlsanity.com/paper/2608.30044/) (2026)
- [LLM Persuasion Is in the Eye of the Evaluation](https://mlsanity.com/paper/2610.10232/) (2026)

## Abstract (arXiv)

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: github.com/IBM/BenchBench Leaderboard: hf.co/spaces/IBM/BenchBench