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Trung Le

ACL 2026×7ICLR 2026×7CVPR 2026×4AAAI 2026×3KDD 2026×2对齐与偏好强化学习与后训练效率与系统检索与知识可解释性
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0 一作 · 23 篇 · ACL 2026 / ICLR 2026 / CVPR 2026 / AAAI 2026 / KDD 2026

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2026 顶会论文

23
作者位次 #3/5ICLR 2026Poster· General Machine Learning / Transfer, Multitask and Meta-learning

Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models

Anh Hoang · Van Cuong Pham · Trung Le · Jianfei Cai · Thanh-Toan Do

对齐与偏好强化学习与后训练效率与系统OpenReview
作者位次 #2/5ICLR 2026Poster· Social Aspects / Trustworthy Machine Learning

Antibody: Strengthening Defense Against Harmful Fine-Tuning for Large Language Models via Attenuating Harmful Gradient Influence

Quoc Nguyen · Trung Le · Jing Wu · Anh Bui · Mehrtash Harandi

安全强化学习与后训练社会与人本OpenReview
作者位次 #6/6ACL 2026Poster· Natural Language Processing

LLM - XTM : Enhancing Cross-Lingual Topic Models with Large Language Models

Minh Chu Xuan · Tien-Phat Nguyen · Linh Ngo Van · Dinh Viet Sang · Nguyen Thi Ngoc Diep · Trung Le

摘要

Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. Recent LLM-based refinements improve interpretability but are costly, document-level, and prone...

多语言与语言学对齐与偏好安全OpenReview 项目页
作者位次 #5/6ACL 2026Poster· Natural Language Processing

MTA : Multi-Granular Trajectory Alignment for Large Language Model Distillation

Pham Khanh Chi · Quoc Phong Dao · Thuat Nguyen · Linh Ngo Van · Trung Le · Thanh Hong Nguyen

摘要

Knowledge distillation is a key technique for compressing large language models (LLMs), but most existing methods align representations at fixed layers or token-level outputs, ignoring how representations evolve across depth. As a result, the student is only weakly guided to capture the teacher’s...

对齐与偏好可解释性强化学习与后训练OpenReview 项目页
作者位次 #7/7ACL 2026Poster· Natural Language Processing

TALAS : Teacher-Anchored Layer Alignment with Adaptive Sharpness-Aware Minimization for Embedding Distillation

Quoc Phong Dao · Hoang-Son Nguyen · Pham Khanh Chi · Linh Ngo Van · Nguyen Thi Ngoc Diep · Thien Huu Nguyen · Trung Le

摘要

Knowledge Distillation (KD) has established itself as a pivotal technique for compressing large pre-trained language models. However, existing methods that force a student to strictly mimic the teacher’s sentence embeddings or internal features often incur prohibitive computational costs and yiel...

效率与系统检索与知识对齐与偏好OpenReview 项目页
作者位次 #7/7ACL 2026Poster· Natural Language Processing

SRA : Span Representation Alignment for Large Language Model Distillation

Quoc Phong Dao · Hoang-Son Nguyen · Pham Khanh Chi · Tung Nguyen · Linh Ngo Van · Nguyen Thi Ngoc Diep · Trung Le

摘要

Cross-Tokenizer Knowledge Distillation (CTKD) enables knowledge transfer between a large language model and a smaller student, even when they employ different tokenizers. While existing approaches mainly focus on token-level alignment strategies, which are often brittle and sensitive to discrepan...

对齐与偏好可解释性强化学习与后训练OpenReview 项目页
作者位次 #4/6ACL 2026Poster· Natural Language Processing

Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models

Cuong Pham · Anh Dung Hoang · Cuong C. Nguyen · Trung Le · Gustavo Carneiro · Thanh-Toan Do

摘要

Large language models (LLMs) have advanced natural language processing, but their massive parameter counts create computational and memory challenges during deployment. Post-training quantization (PTQ) has emerged as a promising approach to mitigate these challenges. While existing PTQ methods ca...

效率与系统强化学习与后训练检索与知识OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

MIPIC : Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining

Phung Gia Huy · Hai An Vu · Minh-Phuc Truong · Thang Duc Tran · Linh Ngo Van · Thanh Hong Nguyen · Trung Le

摘要

Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) offers a flexible inference paradigm through nested embeddings; however, learning such structures requires explicit coo...

对齐与偏好可解释性强化学习与后训练OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

M em ORAI : Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents

Hung Pham Van · Nguyen Manh Hieu · Khang Pham Tran Tuan · Nam Le Hai · Linh Ngo Van · Nguyen Thi Ngoc Diep · Trung Le

摘要

Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. We introduce MemORAI (Memory Organization and Retrieval...

检索与知识评测与基准智能体与工具OpenReview 项目页

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