Summary of Evading Data Contamination Detection For Language Models Is (too) Easy, by Jasper Dekoninck et al.
Evading Data Contamination Detection for Language Models is (too) Easy
by Jasper Dekoninck, Mark Niklas Müller, Maximilian Baader, Marc Fischer, Martin Vechev
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 The proposed research investigates the reliability of public benchmarks for large language models, which are often trained on vast amounts of data. The study reveals that these models can be contaminated with public benchmarks, compromising performance measurements. Moreover, it identifies a previously overlooked threat: malicious model providers intentionally contaminating their models to evade detection. To address this issue, the researchers propose categorizing both model providers and contamination detection methods, exposing vulnerabilities in existing techniques. By exploiting these weaknesses, the authors demonstrate EAL, a simple yet effective technique that significantly inflates benchmark performance while evading current detection methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are trained on huge amounts of data, which can lead to problems with public benchmarks. These models can be “hacked” to make them look better than they really are. The study looks at how this happens and proposes new ways to detect it. It shows that some people might deliberately make their models seem better by hiding the fact that they’re using fake data. This is important because it means we can’t always trust the results of language model tests. |
Keywords
* Artificial intelligence * Language model
