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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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Related Experiment Video

Updated: Oct 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

735

A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation.

Kun Zhao1, Hongwei Ding1, Kai Ye1

  • 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.

Entropy (Basel, Switzerland)
|October 23, 2021
PubMed
Summary

This study introduces a Hierarchical Transformer-Variational AutoEncoder with Hidden Markov Model (HT-HVAE) for improved long text generation. The model effectively captures sentence relationships, producing more coherent and logically connected long texts.

Keywords:
Hidden Markov ModelTransformerVariational AutoEncoderlatent variablestext generation

Related Experiment Videos

Last Updated: Oct 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

735

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Machine Learning

Background:

  • Variational Autoencoders (VAEs) have advanced text generation but are limited to short text outputs.
  • Generating long, coherent text requires understanding relationships between sentences and their underlying latent variables.
  • Few studies explore the crucial relationships between latent variables for long text generation.

Purpose of the Study:

  • To propose a novel method, the Hierarchical Transformer-Variational AutoEncoder with Hidden Markov Model (HT-HVAE), for enhanced long text generation.
  • To effectively learn multiple hierarchical latent variables and their interdependencies.
  • To improve the continuity and logical coherence of generated long texts.

Main Methods:

  • Utilized a hierarchical Transformer encoder to capture intricate hierarchical information within long texts.
  • Integrated a Hidden Markov Model (HMM) within the VAE's generation network to model relationships between latent variables.
  • Developed a method for calculating perplexity specific to the multi-hierarchical latent variable structure.

Main Results:

  • The HT-HVAE model demonstrated superior performance on datasets requiring strong logical consistency.
  • The proposed method successfully alleviated the common posterior collapse problem in VAEs.
  • Generated texts exhibited enhanced continuity and logical flow compared to existing methods.

Conclusions:

  • The HT-HVAE model offers a significant advancement in generating coherent and logically structured long texts.
  • The integration of HMM with hierarchical VAEs effectively models latent variable relationships for improved text generation.
  • This approach addresses key limitations in current VAE-based text generation, particularly for longer, more complex outputs.