Summary of Data Laundering: Artificially Boosting Benchmark Results Through Knowledge Distillation, by Jonibek Mansurov et al.
Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation
by Jonibek Mansurov, Akhmed Sakip, Alham Fikri Aji
First submitted to arxiv on: 15 Dec 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 A subfield-specific paper reveals a critical vulnerability in current language model evaluation practices, showing how knowledge distillation can be used to manipulate benchmark scores. The researchers introduce “Data Laundering,” a three-phase process that enables the covert transfer of benchmark-specific knowledge through seemingly legitimate intermediate training steps. This approach is demonstrated using a 2-layer BERT student model, achieving significant improvements in benchmark accuracy (up to 75% on GPQA) without developing genuine reasoning capabilities. The findings highlight the urgent need for more robust evaluation methods in AI and contribute to the ongoing discussion about evaluation integrity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers discovered a way to trick language models into performing better than they really are. This was done by using a process called “Data Laundering,” which involves training models on specific data to make them look good, but not actually teaching them new skills. The results showed that this method could improve scores by up to 75% without making the model any smarter. The authors think this is important because it shows that current ways of testing language models are flawed and need to be improved. |
Keywords
» Artificial intelligence » Bert » Knowledge distillation » Language model » Student model
