What is recorded here?
Removed the causal attention mask during contrastive embedding training, used token queries attending a trainable latent key/value dictionary before mean pooling, and staged retrieval then mixed-task instruction tuning.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Latent dictionary keys/values are not the queries: this is not simply Perceiver latent-query pooling. Second-stage mixed-task training omits in-batch negatives to avoid class false negatives. Public embedding-training data does not mean the pretrained LLM was trained from scratch with that data; snapshot leaderboard results are not current rankings.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Chankyu Lee; Rajarshi Roy; Mengyao Xu; Jonathan Raiman; Mohammad Shoeybi; Bryan Catanzaro; Wei Ping
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
NV-Embed: latent dictionary attention and staged embedding instruction tuning
Removed the causal attention mask during contrastive embedding training, used token queries attending a trainable latent key/value dictionary before mean pooling, and staged retrieval then mixed-task instruction tuning.
Scientific scope
Latent dictionary keys/values are not the queries: this is not simply Perceiver latent-query pooling. Second-stage mixed-task training omits in-batch negatives to avoid class false negatives. Public embedding-training data does not mean the pretrained LLM was trained from scratch with that data; snapshot leaderboard results are not current rankings.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Chankyu Lee; Rajarshi Roy; Mengyao Xu; Jonathan Raiman; Mohammad Shoeybi; Bryan Catanzaro; Wei Ping
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.