Papers by Huy Le
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. |
NSL-MT: Linguistically Informed Negative Samples for Efficient Machine Translation in African Low-Resource Languages (2026.findings-acl)
Copied to clipboard
| Challenge: | In low-resource settings, models encounter too few examples to reliably distinguish grammatical patterns from noise. |
| Approach: | They propose a negative space learning machine translation (NSL-MT) method that augments limited parallel data with synthetically generated violations of the target language’s grammar and explicitly penalizes the model when it assigns high probability to these violations. |
| Outcome: | The proposed method delivers 3-12% BLEU gains for well-performing models and 56-89% gains for models lacking decent initial support. |
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference (2026.acl-long)
Copied to clipboard
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. |
VisualCoder: Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to enhance large language models' ability to predict program behavior struggle with dynamic reasoning tasks. |
| Approach: | They propose a visual control flow graph that integrates CoT reasoning with a control flow . they aim to improve performance in program behavior prediction, error detection and output generation . |
| Outcome: | The proposed approach improves program behavior prediction, error detection, and output generation. |
Experience as Source for Anticipation and Planning: Experiential Policy Learning for Target-driven Recommendation Dialogues (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to enhance dialogues lack the ability to anticipate user interactions . current approaches lack the capability to anticipate past interactions and to neglect past experiences . |
| Approach: | They propose a framework for enhancing dialogue anticipation with an experiential scoring function that estimates dialogue state potential using similar past interactions stored in long-term memory. |
| Outcome: | Experiments on two datasets show the framework is superior and effective . tree-structured EPL assesses past dialogue states with LLMs and MCTS . |
Reinforced Target-driven Conversational Promotion (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing conversational recommendation methods focus on acquiring user preferences while ignoring strategic planning for nudging users towards accepting a designated item. |
| Approach: | They propose a Reinforced Target-driven Conversational Promotion framework that integrates short-term and long-term planning via a balanced gating mechanism. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation. |
VMLU Benchmarks: A comprehensive benchmark toolkit for Vietnamese LLMs (2025.acl-long)
Copied to clipboard
Cuc Thi Bui, Nguyen Truong Son, Truong Van Trang, Lam Viet Phung, Pham Nhut Huy, Hoang Anh Le, Quoc Huu Van, Phong Nguyen-Thuan Do, Van Le Tran Truc, Duc Thanh Chau, Le-Minh Nguyen
| Challenge: | The evolution of Large Language Models (LLMs) has underscored the need for benchmarks designed for various languages and cultural contexts. |
| Approach: | They propose to use Vietnamese multitask language understanding (VMLU) benchmarks to assess different capabilities of LLMs, including general knowledge, reading comprehension, reasoning, and conversational skills. |
| Outcome: | The VMLU Benchmarks assess LLMs' general knowledge, reading comprehension, reasoning, and conversational skills. |