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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
Beyond Routing Weights: Faithful Response-Level Interpretation of Mixture-of-Experts Reward Models via Contribution Contrast
TL;DR
A novel interpretation method called Contribution-Contrast (CoCo) was developed to enhance the understanding of Mixture-of-Experts (MoE) reward models. CoCo provides clearer insights into expert behavior by analyzing chosen-rejected response pairs, outperforming existing methods in interpretability while maintaining competitive accuracy.
✦ Why It Matters
Implement CoCo to improve the interpretability of your reward models today.
Key Takeaways
- Adopt CoCo for better insights into expert decision-making.
- Utilize chosen-rejected response pairs for analysis.
- Evaluate your models with CoCo for improved interpretability.
- Compare CoCo's performance against existing methods.
How It Works
CoCo analyzes pairs of responses—one that an expert chooses and one that it rejects—to assess the contribution of each response to the expert's decision. This method captures both the routing of prompts to experts and the preferences that guide their evaluations, providing a more comprehensive understanding of expert behavior.