4.3k citations · 10.6k across the 26 of their papers we have counts for
7 papers · 1 filter
Good-Enough Example Extrapolation
Jason Wei
This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation. To operationalize…
Frequency Effects on Syntactic Rule Learning in Transformers
Jason Wei, Dan Garrette, Tal Linzen +1
Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract…
A Recipe For Arbitrary Text Style Transfer with Large Language Models
Emily Reif, Daphne Ippolito, Ann Yuan +3
In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer. We present a prompting method that we call augmented zero-shot learning, which fram…
Language Model Augmented Relevance Score
Ruibo Liu, Jason Wei, Soroush Vosoughi
Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknes…
Modulating Language Models with Emotions
Ruibo Liu, Jason Wei, Chenyan Jia +1
Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated…
A Cognitive Regularizer for Language Modeling
Jason Wei, Clara Meister, Ryan Cotterell
The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across a linguistic signal, has gained trac…
Mitigating Political Bias in Language Models Through Reinforced Calibration
Ruibo Liu, Chenyan Jia, Jason Wei +3
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world…