TEncDM: Understanding the Properties of Diffusion Model in the Space of
Language Model Encodings
- URL: http://arxiv.org/abs/2402.19097v1
- Date: Thu, 29 Feb 2024 12:25:45 GMT
- Title: TEncDM: Understanding the Properties of Diffusion Model in the Space of
Language Model Encodings
- Authors: Alexander Shabalin, Viacheslav Meshchaninov, Tingir Badmaev, Dmitry
Molchanov, Grigory Bartosh, Sergey Markov, Dmitry Vetrov
- Abstract summary: We introduce a novel approach named Text Diffusion Model (TEncDM)
Instead of the commonly used token embedding space, we train our model in the space of the language model encodings.
We also analyse self-conditioning and find that it increases the magnitude of the model outputs.
- Score: 39.34471874948928
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Drawing inspiration from the success of diffusion models in various domains,
numerous research papers proposed methods for adapting them to text data.
Despite these efforts, none of them has managed to achieve the quality of the
large language models. In this paper, we conduct a comprehensive analysis of
key components of the text diffusion models and introduce a novel approach
named Text Encoding Diffusion Model (TEncDM). Instead of the commonly used
token embedding space, we train our model in the space of the language model
encodings. Additionally, we propose to use a Transformer-based decoder that
utilizes contextual information for text reconstruction. We also analyse
self-conditioning and find that it increases the magnitude of the model
outputs, allowing the reduction of the number of denoising steps at the
inference stage. Evaluation of TEncDM on two downstream text generation tasks,
QQP and XSum, demonstrates its superiority over existing non-autoregressive
models.
Related papers
- Low-resource neural machine translation with morphological modeling [3.3721926640077804]
Morphological modeling in neural machine translation (NMT) is a promising approach to achieving open-vocabulary machine translation.
We propose a framework-solution for modeling complex morphology in low-resource settings.
We evaluate our proposed solution on Kinyarwanda - English translation using public-domain parallel text.
arXiv Detail & Related papers (2024-04-03T01:31:41Z) - Exploring Automatic Evaluation Methods based on a Decoder-based LLM for
Text Generation [16.78350863261211]
This paper compares various methods, including tuning with encoder-based models and large language models under equal conditions.
Experimental results show that compared to the tuned encoder-based models, the tuned decoder-based models perform poorly.
It is also revealed that in-context learning of very large decoder-based models such as ChatGPT makes it difficult to identify fine-grained semantic differences.
arXiv Detail & Related papers (2023-10-17T06:53:00Z) - Minimally-Supervised Speech Synthesis with Conditional Diffusion Model
and Language Model: A Comparative Study of Semantic Coding [57.42429912884543]
We propose Diff-LM-Speech, Tetra-Diff-Speech and Tri-Diff-Speech to solve high dimensionality and waveform distortion problems.
We also introduce a prompt encoder structure based on a variational autoencoder and a prosody bottleneck to improve prompt representation ability.
Experimental results show that our proposed methods outperform baseline methods.
arXiv Detail & Related papers (2023-07-28T11:20:23Z) - PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model [37.2192243883707]
We propose PLANNER, a model that combines latent semantic diffusion with autoregressive generation to generate fluent text.
Results on semantic generation, text completion and summarization show its effectiveness in generating high-quality long-form text.
arXiv Detail & Related papers (2023-06-05T01:36:39Z) - Diffusion Models for Non-autoregressive Text Generation: A Survey [94.4634088113513]
Non-autoregressive (NAR) text generation has attracted much attention in the field of natural language processing.
Recently, diffusion models have been introduced into NAR text generation, showing an improved text generation quality.
arXiv Detail & Related papers (2023-03-12T05:11:09Z) - DiffusionBERT: Improving Generative Masked Language Models with
Diffusion Models [81.84866217721361]
DiffusionBERT is a new generative masked language model based on discrete diffusion models.
We propose a new noise schedule for the forward diffusion process that controls the degree of noise added at each step.
Experiments on unconditional text generation demonstrate that DiffusionBERT achieves significant improvement over existing diffusion models for text.
arXiv Detail & Related papers (2022-11-28T03:25:49Z) - N-Grammer: Augmenting Transformers with latent n-grams [35.39961549040385]
We propose a simple yet effective modification to the Transformer architecture inspired by the literature in statistical language modeling, by augmenting the model with n-grams that are constructed from a discrete latent representation of the text sequence.
We evaluate our model, the N-Grammer on language modeling on the C4 data-set as well as text classification on the SuperGLUE data-set, and find that it outperforms several strong baselines such as the Transformer and the Primer.
arXiv Detail & Related papers (2022-07-13T17:18:02Z) - Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting.
Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking.
We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
arXiv Detail & Related papers (2020-10-24T11:55:28Z) - Improve Variational Autoencoder for Text Generationwith Discrete Latent
Bottleneck [52.08901549360262]
Variational autoencoders (VAEs) are essential tools in end-to-end representation learning.
VAEs tend to ignore latent variables with a strong auto-regressive decoder.
We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
arXiv Detail & Related papers (2020-04-22T14:41:37Z) - Abstractive Text Summarization based on Language Model Conditioning and
Locality Modeling [4.525267347429154]
We train a Transformer-based neural model on the BERT language model.
In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size.
The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset.
arXiv Detail & Related papers (2020-03-29T14:00:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.