Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Quick Answer
This study reveals that modern reasoning models excel in zero-shot performance on multi-label tasks by employing a two-phase process: shortlisting candidates followed by fine-grained reasoning.
Quick Take
A new mechanistic distillation strategy developed from this understanding consistently outperforms traditional methods across various datasets.
Key Points
- Modern reasoning models achieve strong zero-shot performance on multi-label tasks.
- The reasoning process consists of shortlisting followed by detailed analysis.
- The new distillation strategy outperforms standard methods across various datasets.
- Findings suggest that the two phases of reasoning are complementary.
- This work enhances understanding of mechanistic reasoning in large output spaces.
Paper Resources
Source Excerpt
Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevant options from hundreds of thousands to millions of candidate labels. We investigate how they achieve this mechanistically. We characterize reasoning as a two-phase process: A broad "shortlisting" of candidates followed by fine-grained reasoning over the resulting set. We provide evidence across a range of datasets that these steps can be isolated
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