EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 5·5 min read
Your model already knows the answer: how benchmark answers leak into LLMs
TL;DR
AI models can inadvertently cheat on benchmarks by recalling known answers from their training data, leading to misleadingly high performance scores. This contamination problem complicates the evaluation of models in fields like drug trial predictions.
✦ Why It Matters
Evaluate your model's benchmarks to ensure they do not rely on publicly known outcomes.
Key Takeaways
- Use date-gated tools to prevent input leaks.
- Test models on fresh datasets to avoid benchmark leaks.
- Implement recognition tests to assess reasoning versus memorization.
- Focus on outcome leak methods for historical data evaluations.
How It Works
To prevent leaks, researchers suggest using date-gated tools that restrict access to documents published after a decision date. This ensures that models cannot access future outcomes during evaluation.
Additionally, fresh test sets can be created from cases that were not included in the model's training data, thereby eliminating benchmark leaks.