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About this paper
Ramnath Kumar, Prateek Jain, Cho-Jui Hsieh
FastLane introduces a framework that enhances late-interaction dense retrieval models by employing a learnable dynamic routing mechanism to select the most informative query token representation. This approach maintains competitive retrieval accuracy while substantially improving computational efficiency and enabling compatibility with Approximate Nearest Neighbor Search (ANNS) libraries.
Key insights
- Many token embeddings generated by late-interaction models for a query are semantically redundant; a single, carefully selected token view can often approximate overall relevance.
- A learnable, dynamic routing mechanism can effectively identify and utilize the most informative query token view without significant loss in retrieval performance.
- Gumbel-Softmax reparameterization allows for end-to-end training of a discrete view selection process, enabling gradient flow through the selection of a specific query representation.
Summary by alphaXiv.
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