Papers by Huy Tran
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining (2026.findings-acl)
Copied to clipboard
Phung Gia Huy, Hai An Vu, Minh-Phuc Truong, Thang Duc Tran, Linh Ngo Van, Thanh Hong Nguyen, Trung Le
| 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. |
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (2025.acl-demo)
Copied to clipboard
Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, Yen-Sung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Yu-Yen Cheng, Karen Farley, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A. Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, Jesse Dodge
| Challenge: | tracing language models' outputs back to training data is a problem because they are trained on text corpora with trillions of tokens . existing methods for tracers have not been scaled to work within this multi-trillion-token setting . |
| Approach: | They propose a system that traces language models' outputs verbatim back to training data . OLMOTRACE retrieves documents from the model's training data that contain exact matches . |
| Outcome: | The proposed system can find verbatim matches between LM output and training data . it can be used to explore fact checking, hallucination, and creativity of language models . |
VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to machine translation (MT) systems degrade when faced with code-mixed text. |
| Approach: | They propose a system that can augment Vietnamese-English code-mixed text with iterative fine-tuning and targeted filtering. |
| Outcome: | The proposed framework outperforms strong back-translation baselines and improves zero-shot models by up to +11.9 points. |