CapBencher: Give Your LLM Benchmark a Built-in Alarm for Test-Set Overfitting
CapBencher reduces Bayes accuracy by randomizing answers to detect LLM test-set overfitting.
Key Findings
Methodology
CapBencher introduces randomness in answers to reduce Bayes accuracy, avoiding full disclosure of true answers. It prepares multiple logically correct answers for each question and randomly selects one as the benchmark solution. This method obscures true answers and provides a test for data leakage or gaming.
Key Results
- CapBencher successfully detected test-set overfitting across various benchmarks and models, with model accuracy not exceeding Bayes accuracy.
- Experiments show CapBencher accurately identifies data leakage even with different training methodologies and scenarios.
- By setting Bayes accuracy at 50%, CapBencher maintained consistent model rankings across multiple datasets.
Significance
CapBencher offers a novel way to publish LLM benchmarks, preserving open evaluation while detecting data leakage and gaming. This method is significant in academia and industry as it addresses the prevalent issue of overfitting in current benchmarks.
Technical Contribution
CapBencher innovates on existing methods by introducing randomness to reduce Bayes accuracy, providing new theoretical guarantees and engineering possibilities. Unlike existing dynamic benchmarking methods, it applies to any problem.
Novelty
CapBencher is the first to detect test-set overfitting by randomizing answers to reduce Bayes accuracy, a fundamental innovation compared to existing benchmarking methods.
Limitations
- CapBencher may cause unstable rankings on small datasets due to increased variance from randomization.
- Larger models may be needed to detect data leakage in some cases.
Future Work
Future research could explore applying CapBencher in more complex scenarios and further reducing Bayes accuracy to enhance detection capabilities.
AI Executive Summary
Test-set overfitting is a common issue in evaluating large language models (LLMs). Existing solutions often require benchmarks to remain private, but this does not fully prevent overfitting through feedback loops. CapBencher offers a new solution by introducing randomness in answers to reduce Bayes accuracy.
The core idea of CapBencher is to prepare multiple logically correct answers for each question and randomly select one as the benchmark solution. This method not only obscures true answers but also provides a test for data leakage or gaming. Experiments demonstrate that CapBencher can successfully detect test-set overfitting across various benchmarks and models.
This method is significant in academia and industry as it addresses the prevalent issue of overfitting in current benchmarks. Future research could explore applying CapBencher in more complex scenarios and further reducing Bayes accuracy to enhance detection capabilities.
Deep Analysis
Background
As LLM capabilities increase, constructing modern benchmarks becomes more expensive. For example, datasets like FrontierMath and Humanity’s Last Exam require extensive expert involvement. However, publishing these benchmarks' true answers risks contaminating future models and incentivizing test-set overfitting.
Core Problem
Current benchmarking methods often require privacy, but this does not fully prevent overfitting through feedback loops. The challenge is to maintain open evaluation while detecting data leakage without fully disclosing true answers.
Innovation
CapBencher offers a novel solution by introducing randomness in answers to reduce Bayes accuracy. Its core innovation lies in preparing multiple logically correct answers for each question and randomly selecting one as the benchmark solution.
Methodology
- �� Prepare multiple logically correct answers for each question.
- �� Randomly select one answer as the benchmark solution.
- �� Reduce Bayes accuracy to detect data leakage or gaming.
- �� Maintain consistent model rankings.
Experiments
The experimental design includes various datasets and models to test CapBencher's effectiveness across different scenarios. By setting Bayes accuracy at 50%, it verifies the ability to maintain consistent model rankings across multiple datasets.
Results
Experiments show CapBencher successfully detected test-set overfitting across various benchmarks and models, with model accuracy not exceeding Bayes accuracy. It accurately identifies data leakage even with different training methodologies and scenarios.
Applications
CapBencher can be applied to any benchmarking scenario requiring data leakage and gaming detection, especially when open evaluation capabilities need to be preserved.
Limitations & Outlook
CapBencher may cause unstable rankings on small datasets due to increased variance from randomization. Larger models may be needed to detect data leakage in some cases.
Plain Language Accessible to non-experts
Imagine you're taking a school test, and the teacher gives you questions with slightly altered answers, like adding or subtracting a point. This way, even if you cheat, you can't get a perfect score because you don't know which answer is correct. That's the principle behind CapBencher, which detects cheating by randomizing answers.
ELI14 Explained like you're 14
Imagine you're playing a game where you have to guess a number, but the number might be randomly increased or decreased by one. This makes the game more challenging because even if you know the answer, you can't guarantee a perfect score. That's how CapBencher works, ensuring no one can easily cheat by randomizing answers.
Glossary
Bayes Accuracy
The accuracy of the best decision rule for a given problem.
CapBencher reduces Bayes accuracy to detect overfitting.
Randomization
The process of introducing randomness to alter answers.
Used to obscure true answers and reduce Bayes accuracy.
Test-set Overfitting
When a model performs well on a test set but poorly in real-world applications.
CapBencher aims to detect this phenomenon.
Data Leakage
The phenomenon where training data contains information from the test set.
CapBencher can detect data leakage.
Benchmarking
Standardized tests used to evaluate model performance.
CapBencher offers a new way to publish benchmarks.
Open Questions Unanswered questions from this research
- 1 How can CapBencher be applied in more complex scenarios?
- 2 How can Bayes accuracy be further reduced to enhance detection capabilities?
Applications
Immediate Applications
Benchmark Publication
Researchers can use CapBencher to publish benchmarks, avoiding data leakage.
Long-term Vision
Standardizing Model Evaluation
CapBencher could become a new standard for model evaluation, ensuring fairness and accuracy.
Abstract
Publishing a large language model (LLM) benchmark (especially its ground-truth answers) on the Internet risks contaminating future LLMs and enabling evaluation gaming: it may be unintentionally (or intentionally) used to train or select a model, or exploited to overfit and hack leaderboards when labels are accessible. A common mitigation is to keep the benchmark private and let participants submit their models or predictions to the organizers, but this still permits test-set overfitting through feedback loops. To overcome this issue, we propose CapBencher, a way to publish benchmarks without fully disclosing the ground-truth answers, while preserving open evaluation of LLMs. The main idea is to reduce the best possible accuracy, i.e., Bayes accuracy, by injecting randomness to the answers by preparing several logically correct answers, and only include one of them as the solution in the benchmark. Not only does this obscure the ground-truth answers, but it also offers a test for leakage or gaming: since even fully capable models should not surpass the Bayes accuracy, any model that does is a strong signal. We show theoretically and empirically that CapBencher accurately detects test-set overfitting across diverse benchmarks, models, training methodologies, and scenarios.