UEmbed: Unified Sparse and Dense Multimodal Embeddings
Researchers present UEmbed, a decoder‑only model that generates both sparse lexical and dense embeddings for multimodal data in a single forward pass.
- UEmbed combines sparse and dense representations in one decoder‑only model.
- It eliminates the need for separate cross‑modal modules in multimodal retrieval.
- The approach may lower inference costs and simplify retrieval system design.
Sparse retrieval is a core component of modern search systems, from web engines to retrieval‑augmented generation. Recent work on Learned Sparse Retrieval (LSR) has tried to move beyond exact lexical matching, but most approaches rely on encoder‑style bidirectional models.
Extending LSR to multimodal data has required additional cross‑modal modules, adding complexity and latency. This limits the practicality of sparse methods for images, audio, or video alongside text.
UEmbed (Unified Embedding) addresses these gaps with a decoder‑only architecture. In a single causal forward pass, it produces both sparse lexical vectors and dense embeddings for multimodal inputs, eliminating the need for separate encoders or auxiliary modules.
The unified approach could streamline retrieval pipelines, reduce inference costs, and broaden the use of sparse methods in multimodal AI applications such as image search, video captioning, and cross‑modal question answering.
Provides a single model for both sparse and dense multimodal embeddings, simplifying integration.
Reduces infrastructure overhead for search and retrieval services that handle multiple data types.
Highlights a novel direction that could improve efficiency of AI products relying on multimodal search.
Introduces a fresh research avenue for studying unified embedding architectures.
Shows progress toward more efficient AI systems that can understand and retrieve across text, images, and audio.
- Sparse Retrieval
- A search technique that uses high‑dimensional sparse vectors to match queries with documents based on lexical overlap.
- Decoder‑only model
- An architecture that generates outputs using only a causal (autoregressive) transformer, without a separate encoder.
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