3 papers
cs.CL2025
Natural Language Satisfiability: Exploring the Problem Distribution and Evaluating Transformer-based Language Models
Tharindu Madusanka, Ian Pratt-Hartmann, Riza Batista-Navarro
Efforts to apply transformer-based language models (TLMs) to the problem of reasoning in natural language have enjoyed ever-increasing success in recent years. The most fundamental…
cs.CL2024
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders
Yingji Zhang, Danilo S. Carvalho, Marco Valentino +2
Achieving precise semantic control over the latent spaces of Variational AutoEncoders (VAEs) holds significant value for downstream tasks in NLP as the underlying generative mechan…
cs.CL2023
LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces
Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann +1
Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-lev…