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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Memorization Diagnostics for Code LLMs Should be Scale-Aware
TL;DR
Large language models (LLMs) for code often rely on memorization, but traditional probing techniques fail to effectively evaluate this at scale. By separating representation load from memorization, the study suggests that the usability of LLMs is more important than whether a solution was memorized.
✦ Why It Matters
Focus on evaluating LLMs based on their generalization abilities rather than memorization.
Key Takeaways
- Evaluate LLMs by their ability to generalize solutions.
- Use invertible transforms to assess representation load.
- Rethink probing techniques for larger models.
- Separate memorization from representation load in evaluations.
How It Works
The study applies invertible mathematical transforms to numeric problems, allowing researchers to isolate representation load from memorization. This approach reveals that larger models can effectively handle diverse input forms while still producing correct outputs.