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Found 7 papers, 2 papers with code
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders
Achieving precise semantic control over the latent spaces of Variational AutoEncoders (VAEs) holds significant value for downstream tasks in NLP as the underlying generative mechanisms could be better localised, explained and improved upon.
LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces
Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces.
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to result in an overall improvement of performances and generalisation.
Towards Controllable Natural Language Inference through Lexical Inference Types
They employ the T5 model to directly generate the tree, which can explain how the answer is inferred.
Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation.
Quasi-symbolic explanatory NLI via disentanglement: A geometrical examination
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control, and understanding downstream task performance in Natural Language Processing.
Learning Disentangled Representations for Natural Language Definitions
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing.
