38 citations · 86 across the 4 of their papers we have counts for
6 papers
Calibrating Sequence likelihood Improves Conditional Language Generation
Yao Zhao, Misha Khalman, Rishabh Joshi +3
Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained mod…
TALM: Tool Augmented Language Models
Aaron Parisi, Yao Zhao, Noah Fiedel
Transformer based language models (LMs) demonstrate increasing performance with scale across a wide variety of tasks. Scale alone however cannot enable models to solve tasks that r…
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
Shashi Narayan, Gonçalo Simões, Yao Zhao +4
We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding str…
Planning with Learned Entity Prompts for Abstractive Summarization
Shashi Narayan, Yao Zhao, Joshua Maynez +3
We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries…
SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
Yao Zhao, Mohammad Saleh, Peter J. Liu
Most prior work in the sequence-to-sequence paradigm focused on datasets with input sequence lengths in the hundreds of tokens due to the computational constraints of common RNN an…
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh +1
Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summariza…