71 citations · 267 across the 20 of their papers we have counts for
16 papers · 1 filter
With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation
Y. Wang, D. Ma, D. Cai
Long text generation, such as novel writing and discourse-level translation with extremely long contexts, presents significant challenges to current language models. Existing metho…
Copy Is All You Need
Tian Lan, Deng Cai, Yan Wang +2
The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary. In this paper, we formulate text generation as progressively copying…
PandaGPT: One Model To Instruction-Follow Them All
Yixuan Su, Tian Lan, Huayang Li +3
We present PandaGPT, an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities. Our pilot experiments show that PandaGPT can perform…
A Survey on Retrieval-Augmented Text Generation
Huayang Li, Yixuan Su, Deng Cai +2
Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrieval-aug…
Exploring Dense Retrieval for Dialogue Response Selection
Tian Lan, Deng Cai, Yan Wang +3
Recent progress in deep learning has continuously improved the accuracy of dialogue response selection. In particular, sophisticated neural network architectures are leveraged to c…
Transductive Learning for Unsupervised Text Style Transfer
Fei Xiao, Liang Pang, Yanyan Lan +3
Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and…