Papers by Vu Hoang

4 papers
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference (2026.acl-long)

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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.
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation (2024.naacl-long)

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Challenge: Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors .
Approach: They propose a framework that can detect and explain toxic speech using a target group generator and an encoder-decoder model.
Outcome: The proposed model outperforms baseline models and achieves state-of-the-art effectiveness . the proposed model generates a toxic explanation that matches the ground truth explanation .
Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)

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Challenge: Large language models (LLMs) have shown impressive performance in code understanding and generation.
Approach: They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks.
Outcome: The proposed models have shown to be highly effective in coding tasks.
ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining (2022.lrec-1)

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Challenge: Recent large-scale language models show remarkable achievements in key NLP tasks such as Question Answering and Text Summarization.
Approach: They propose a domain-specific pre-trained Vietnamese language model that outperforms the general domain language models.
Outcome: The proposed model outperforms the general domain language models in Vietnamese datasets while outperforming the general-domain language models.

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