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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 7·5 min read
Shared circuits predict whether LLMs generalize across formats in arithmetic reasoning
TL;DR
Shared circuits in large language models (LLMs) can predict their ability to generalize arithmetic reasoning across different input formats. The study reveals that models perform better on verbal arithmetic when their numeric circuits overlap significantly with those used for numeric problems.
✦ Why It Matters
Evaluate your LLM's circuit overlap to improve its performance on verbal arithmetic tasks today.
Key Takeaways
- Investigate circuit overlap to enhance LLM performance on verbal tasks.
- Utilize attribution patching to analyze model internals effectively.
- Focus on training models with diverse input formats for better generalization.
- Consider language-specific performance variations when deploying LLMs.
How It Works
Attribution patching is used to identify which internal circuits of LLMs are activated for solving numeric versus verbal arithmetic problems. By analyzing these circuits, researchers can determine how well a model can generalize from numeric to verbal formats based on the overlap of the circuits involved.