83 citations · 380 across the 28 of their papers we have counts for
9 papers · 1 filter
Plug-and-Play Recipe Generation with Content Planning
Yinhong Liu, Yixuan Su, Ehsan Shareghi +1
Recent pre-trained language models have shown promising capabilities in generating fluent and realistic natural language text. However, generating multi-sentence text with global c…
Momentum Decoding: Open-ended Text Generation As Graph Exploration
Tian Lan, Yixuan Su, Shuhang Liu +2
Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. However, maximization-based decoding methods (e.g., gr…
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text Generation
Yixuan Su, Jialu Xu
In the study, we empirically compare the two recently proposed decoding methods, i.e. Contrastive Search (CS) and Contrastive Decoding (CD), for open-ended text generation. The aut…
Contrastive Search Is What You Need For Neural Text Generation
Yixuan Su, Nigel Collier
Generating text with autoregressive language models (LMs) is of great importance to many natural language processing (NLP) applications. Previous solutions for this task often prod…
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
Yutao Zhu, Jian-Yun Nie, Yixuan Su +3
Contextual information in search sessions is important for capturing users' search intents. Various approaches have been proposed to model user behavior sequences to improve docume…
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…