GEM: Generative Embedding Model Reasons Before Retrieval
_reachsumit · x · 2026-08-14
To address the limitation of conventional retrievers relying on surface-level matching, the paper introduces GEM, a Generative Embedding Model.
Core Methodology:
- Unifies generation and embedding within a single model.
- The model explicitly reasons over the query to understand user intent and relevance criteria.
- Appends an embedding token to encode the enriched context for retrieval.
Performance:
On reasoning-intensive and instruction-following retrieval tasks, GEM's reasoning-augmented retrieval outperforms its non-reasoning variants and matches significantly larger baselines. It also supports test-time compute scaling via prompting.
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