71 citations · 267 across the 20 of their papers we have counts for
21 papers
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…
Automatic Prosody Annotation with Pre-Trained Text-Speech Model
Ziqian Dai, Jianwei Yu, Yan Wang +5
Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability. However, the acquisition of prosodic boundary labels relies on…
Language Models Can See: Plugging Visual Controls in Text Generation
Yixuan Su, Tian Lan, Yahui Liu +5
Generative language models (LMs) such as GPT-2/3 can be prompted to generate text with remarkable quality. While they are designed for text-prompted generation, it remains an open…
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…