Summary of Towards Data Contamination Detection For Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges, by Vinay Samuel et al.
Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
by Vinay Samuel, Yue Zhou, Henry Peng Zou
First submitted to arxiv on: 16 Sep 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 The paper investigates the effectiveness of data contamination detection methods in large language models (LLMs) by evaluating five approaches with four state-of-the-art LLMs on eight challenging datasets. The authors reveal limitations in current methods, difficulties in detecting contamination introduced during instruction fine-tuning, and inconsistencies between techniques. These findings highlight the complexity of contamination detection in advanced LLMs, emphasizing the need for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting really good at understanding and generating human-like text. But researchers want to know if these models are just memorizing data or actually learning general rules. To answer this question, scientists have developed ways to detect when a model has been “contaminated” with bad information. However, they’re not sure which of these methods is the best or how well they work on really hard tasks. |
Keywords
» Artificial intelligence » Fine tuning
