15 citations · 21 across the 6 of their papers we have counts for
11 papers · 1 filter
Tree Prompting: Efficient Task Adaptation without Fine-Tuning
John X. Morris, Chandan Singh, Alexander M. Rush +2
Prompting language models (LMs) is the main interface for applying them to new tasks. However, for smaller LMs, prompting provides low accuracy compared to gradient-based finetunin…
Model Criticism for Long-Form Text Generation
Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush
Language models have demonstrated the ability to generate highly fluent text; however, it remains unclear whether their output retains coherent high-level structure (e.g., story pr…
Low-Rank Constraints for Fast Inference in Structured Models
Justin T. Chiu, Yuntian Deng, Alexander M. Rush
Structured distributions, i.e. distributions over combinatorial spaces, are commonly used to learn latent probabilistic representations from observed data. However, scaling these m…
Cascaded Text Generation with Markov Transformers
Yuntian Deng, Alexander M. Rush
The two dominant approaches to neural text generation are fully autoregressive models, using serial beam search decoding, and non-autoregressive models, using parallel decoding wit…
Residual Energy-Based Models for Text Generation
Yuntian Deng, Anton Bakhtin, Myle Ott +2
Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models whi…
Residual Energy-Based Models for Text
Anton Bakhtin, Yuntian Deng, Sam Gross +3
Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations o…