Papers by Huy Vu
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining (2026.findings-acl)
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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. |
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference (2026.acl-long)
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Cuong Chi Le, Minh V.t. Pham, Tung D. Vu, Van Duc Cuong, Phan Nhat Huy, Phan Nhat Hoang, Tien N. Nguyen
| Challenge: | Existing methods for generating specifications are limited and often fail to infer semantic specifications such as pre-/postconditions. |
| Approach: | They propose a framework that treats LLMs as exploratory reasoners rather than one-shot generators. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in accuracy and completeness of generated postconditions. |
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality (2021.naacl-main)
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| Challenge: | In human-level NLP tasks, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within transformer-based language models. |
| Approach: | They propose to use dimension reduction methods to fine-tune large models with limited data and to use pre-trained dimension reduction regimes to improve model performance. |
| Outcome: | The proposed model outperforms other models in human-level NLP tasks with a pre-trained dimension reduction regime. |
Predicting Responses to Psychological Questionnaires from Participants’ Social Media Posts and Question Text Embeddings (2020.findings-emnlp)
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| Challenge: | Existing data cannot be used to predict responses for new questions or participants. |
| Approach: | They propose a method that uses social media texts and the text of the question to predict a participant's questionnaire response. |
| Outcome: | The proposed method can be used to integrate new participants or new questions into psychological studies without costly data collection. |