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Updated: Sep 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Building a Vietnamese Dataset for Natural Language Inference Models.

Chinh Trong Nguyen¹, Dang Tuan Nguyen²

¹University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam.

SN Computer Science

|August 1, 2022

Summary

We developed a new method to create high-quality Vietnamese natural language inference datasets. This approach significantly improves the performance of Vietnamese inference models, achieving 94.79% accuracy.

Keywords:

NLI dataset Natural language inference Textual entailment Transfer learning

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Published on: April 14, 2023

Area of Science:

Natural Language Processing
Machine Learning
Computational Linguistics

Background:

Natural language inference (NLI) models are crucial for natural language understanding (NLU) tasks.
State-of-the-art NLI models require high-quality annotated datasets for training and fine-tuning.
Existing datasets may not be optimized for specific languages like Vietnamese, potentially leading to suboptimal model performance.

Purpose of the Study:

To propose a novel method for constructing a Vietnamese dataset tailored for training Vietnamese NLI models.
To address challenges in Vietnamese text processing, specifically the removal of cue marks and ensuring appropriate writing style.
To enhance the semantic understanding capabilities of NLI models by minimizing reliance on superficial textual cues.

Main Methods:

Developed a data preprocessing technique to remove cue marks from Vietnamese text, preventing models from learning spurious correlations.
Implemented a method to ensure the natural writing style of Vietnamese texts within the dataset.
Fine-tuned a BERT-based model (viNLI) on the newly created Vietnamese dataset.

Main Results:

The viNLI model, trained on the proposed dataset, achieved an accuracy of 94.79% on a Vietnamese test set.
In comparison, a BERT model (viXNLI) fine-tuned on the XNLI dataset achieved only 64.04% accuracy on the same test set.
In an answer selection experiment, viNLI demonstrated superior performance with a P@1 score of 0.4949 compared to viXNLI's 0.4044.

Conclusions:

The proposed method is effective in building a high-quality Vietnamese natural language inference dataset.
The dataset enables the development of significantly more accurate and robust Vietnamese NLI models.
This work contributes to advancing NLU applications for the Vietnamese language.