Papers by Vu Le
HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning (2025.findings-acl)
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| Challenge: | Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models, and limited modeling of hierarchical topic structures. |
| Approach: | They propose a framework that integrates hierarchical clustering and contrastive learning to refine document-topic relationships using compact PLM-based embeddings. |
| Outcome: | The proposed framework improves topic coherence, topic performance, representation quality and computational efficiency over existing NTMs. |
Solving Data-centric Tasks using Large Language Models (2024.findings-naacl)
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Shraddha Barke, Christian Poelitz, Carina Negreanu, Benjamin Zorn, José Cambronero, Andrew Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, Jack Williams
| Challenge: | Large language models are increasingly useful for data-centric tasks, but how do we decide how much data to include in the prompt? |
| Approach: | They propose a cluster-then-select prompting technique that adds the most representative rows from the input data to the LLM prompt. |
| Outcome: | The proposed technique outperforms a baseline for tasks with syntactic variation in the input table. |
One-to-many testing for code generation from (just) natural language (2024.findings-emnlp)
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| Challenge: | MBPP relies on test cases to generate the right signature, data contamination is a problem . adapted code generation benchmarks allow for the description to be underspecified with respect to syntactic properties of code. |
| Approach: | They propose a code generation benchmark that allows for the description to be underspecified with respect to syntactic properties of code. |
| Outcome: | The proposed model removes ambiguity about the semantics of the task from the descriptions and evaluates generated code on multiple sets of assertions to account for ambiguities in the syntax. |
EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments (2025.emnlp-main)
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| Challenge: | Existing methods for knowledge distillation focus on direct output alignment, neglecting this crucial structural information. |
| Approach: | They propose a framework for knowledge distillation that maps tokens one-to-one and aligns attention matrix patterns using Centered Kernel Alignment. |
| Outcome: | The proposed framework significantly outperforms existing CTKD baselines. |
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. |
TSTR: Target Similarity Tuning Meets the Real World (2023.findings-emnlp)
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| Challenge: | Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs). |
| Approach: | They propose to use sentences from a larger language model to improve similarity between two NL inputs and associated code outputs. |
| Outcome: | The proposed model can be trained on a small number of training examples and is cost-effective. |
An empirical study of validating synthetic data for formula generation (2025.findings-naacl)
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Usneek Singh, José Cambronero, Sumit Gulwani, Aditya Kanade, Anirudh Khatry, Vu Le, Mukul Singh, Gust Verbruggen
| Challenge: | Large language models (LLMs) can be leveraged to help write formulas in spreadsheets, but formula data resources are scarce, limiting the ability to fine-tune them. |
| Approach: | They validate a corpus of formulas with a model to generate synthetic natural language utterances for fine-tuning. |
| Outcome: | The proposed model generates synthetic natural language utterances with a model that is accurate enough to fine-tune them. |
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. |
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation (2024.findings-acl)
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Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong
| Challenge: | Modern large language models often "hallucinate" plausible but factually incorrect information, which reduces their trustworthiness especially in settings where accurate and up-to-date information is critical. |
| Approach: | They develop a human evaluation procedure to measure correctness and hallucination and use it to benchmark both closed and open-source LLMs. |
| Outcome: | The proposed method outperforms both competing search engine-augmented prompting methods and commercial systems on search-augmented QA. |
MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora (2025.emnlp-main)
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| Challenge: | Existing approaches to update model-based indexes with new documents are expensive and require expensive retraining. |
| Approach: | They propose a framework that combines an expandable mixture of Low-Rank Adaptation experts with a layer-wise out-of-distribution-driven expansion strategy. |
| Outcome: | Experiments on NQ320k and MS MARCO Passage show that the proposed framework outperforms full-model update baselines with minimal parameter overhead and substantially lower training costs. |
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning (2021.emnlp-main)
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| Challenge: | Recent advances in NLP demonstrate the effectiveness of applying large-scale pre-trained language models to downstream tasks. |
| Approach: | They propose a method that uses task augmentation to fine-tune unlabeled data. |
| Outcome: | The proposed approach improves sample efficiency across 12 few-shot benchmarks. |
Multimodal Review Generation with Privacy and Fairness Awareness (2020.coling-main)
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| Challenge: | Existing frameworks for generating personalized reviews take privacy and fairness into account . users generate digital footprints when "traveling" on the internet . |
| Approach: | They propose a neural-based framework that generates personalized reviews with privacy and fairness in mind. |
| Outcome: | The proposed framework generates plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased embeddings. |
Large-scale Exploration of Neural Relation Classification Architectures (D18-1)
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| Challenge: | Existing studies on relation classification have been limited to a very narrow range of datasets, making comparisons between approaches difficult. |
| Approach: | They propose a multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets and provides direct insights into the challenges faced by language models on relation classification. |
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling (2025.naacl-industry)
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| Challenge: | Existing methods that only deal with flat text chunks use a graph structure to handle complex questions. |
| Approach: | They propose layout-aware graph modeling for multimodal RAG using document layout parsing to take into account relationship of multimodalities. |
| Outcome: | The proposed method can handle complex questions that require information from multimodalities. |
Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs (2025.findings-emnlp)
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| Challenge: | Experimental results demonstrate that our model outperforms existing approaches for summarizing documents. |
| Approach: | proposed model constructs a heterogeneous graph to represent a document and its relevant external citations. |
| Outcome: | The proposed model outperforms existing models in three different scenarios. |
TeCoFeS: Text Column Featurization using Semantic Analysis (2025.findings-naacl)
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| Challenge: | Existing methods for topic modeling and feature extraction are based on syntactic features and overlook the semantics. |
| Approach: | They propose a semantic text column featurization problem that extracts a small sample smartly using an LLM to label only the sample and then extends that labeling to the whole column using text embeddings. |
| Outcome: | The proposed approach performs better than baselines and naive use of LLMs. |
RAR: Retrieval-augmented retrieval for code generation in low resource languages (2024.emnlp-main)
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| Challenge: | Either examples or documentation are commonly used for improved code generation. |
| Approach: | They propose retrieval augmented retrieval as a two-step method for selecting relevant examples and documentation. |
| Outcome: | The proposed method outperforms example and grammar retrieval on low-resource languages . it also outperformed two-step retrieval when used independently . |
CodeFusion: A Pre-trained Diffusion Model for Code Generation (2023.emnlp-main)
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| Challenge: | Existing models for code generation from natural language do not allow reconsidering earlier tokens . prior work has explored grouped beam search or nucleus sampling to generate diverse text. |
| Approach: | They propose a diffusion code generation model that iteratively denoises a program conditioned on the encoded natural language. |
| Outcome: | The proposed model outperforms state-of-the-art models in accuracy and diversity compared to existing models. |
DWA-KD: Dual-Space Weighting and Time-Warped Alignment for Cross-Tokenizer Knowledge Distillation (2026.findings-eacl)
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| Challenge: | Existing cross-tokenizer distillation methods are limited by suboptimal alignment across sequence and vocabulary levels. |
| Approach: | They propose a cross-tokenizer distillation framework that enhances token-wise distillation . they use dual-space entropy-based weighting to achieve precise sequence-level alignment . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in large language models but has high computational and memory costs. |