Summary of Pacost: Paired Confidence Significance Testing For Benchmark Contamination Detection in Large Language Models, by Huixuan Zhang et al.
PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models
by Huixuan Zhang, Yun Lin, Xiaojun Wan
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have become the cornerstone of modern natural language processing, but their performance can be skewed by data contamination from popular benchmarks. This phenomenon leads to inflated scores on leaderboards and disappointing real-world results. To combat this issue, we propose a set of requirements for effective contamination detection methods. Our approach, Paired Confidence Significance Testing (PaCoST), constructs a counterpart for each dataset with the same distribution and conducts statistical analysis to identify significant confidence differences between the original benchmark and the model’s performance. We validate PaCoST’s effectiveness on open-source models and benchmarks, revealing that nearly all tested models and benchmarks are suspected contaminated to varying degrees. This paper calls for new LLM evaluation methods to ensure more accurate assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re training a computer program to understand human language. You want it to be good at answering questions correctly. But what if the data you use to train it is fake or biased? That’s what happens when large language models are trained on data from popular tests, which can make them seem smarter than they really are. Our solution is called PaCoST. It creates a copy of each dataset and checks how confident the model is in its answers. We tested PaCoST on many models and found that almost all of them were contaminated with fake or biased data. This means we need new ways to test language models so we can trust their results. |
Keywords
» Artificial intelligence » Natural language processing
