1.3k citations · 2.8k across the 25 of their papers we have counts for
6 papers · 1 filter
Character-Aware Models Improve Visual Text Rendering
Rosanne Liu, Dan Garrette, Chitwan Saharia +7
Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack cha…
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation
Parker Riley, Timothy Dozat, Jan A. Botha +5
We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation. The dataset consists of professional tr…
Bidirectional Language Models Are Also Few-shot Learners
Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli +3
Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples. An arbitr…
Reducing Retraining by Recycling Parameter-Efficient Prompts
Brian Lester, Joshua Yurtsever, Siamak Shakeri +1
Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
Tu Vu, Aditya Barua, Brian Lester +3
In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case…