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Found 7 papers, 7 papers with code
Diffusion Guided Language Modeling
For auto-regressive language models, existing guidance methods are prone to decoding errors that cascade during generation and degrade performance.
Correction with Backtracking Reduces Hallucination in Summarization
The results show that CoBa is effective and efficient in reducing hallucination, and offers great adaptability and flexibility.
IncDSI: Incrementally Updatable Document Retrieval
Differentiable Search Index is a recently proposed paradigm for document retrieval, that encodes information about a corpus of documents within the parameters of a neural network and directly maps queries to corresponding documents.
Learning Iterative Neural Optimizers for Image Steganography
Image steganography is the process of concealing secret information in images through imperceptible changes.
Latent Diffusion for Language Generation
We then demonstrate that continuous diffusion models can be learned in the latent space of the language autoencoder, enabling us to sample continuous latent representations that can be decoded into natural language with the pretrained decoder.
Fixed Neural Network Steganography: Train the images, not the network
Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images.
BERTScore: Evaluating Text Generation with BERT
We propose BERTScore, an automatic evaluation metric for text generation.
