Papers by Thang Duc Tran

1 papers
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining (2026.findings-acl)

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Challenge: Existing approaches to train dense representations require explicit coordination of how information is arranged across embedding dimensionality and model depth.
Approach: They propose a framework that trains Matryoshka representations using self-distilled intra-relational alignment and Progressive information chaining.
Outcome: The proposed framework produces coherent and compact Matryoshka representations with significant performance advantages under low-dimensional models.

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