67 citations · 142 across the 6 of their papers we have counts for
16 papers · 1 filter
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
Robert L. Logan, Ivana Balažević, Eric Wallace +3
Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. In this work, we show that finetuni…
Detoxifying Language Models Risks Marginalizing Minority Voices
Albert Xu, Eshaan Pathak, Eric Wallace +3
Language models (LMs) must be both safe and equitable to be responsibly deployed in practice. With safety in mind, numerous detoxification techniques (e.g., Dathathri et al. 2020;…
Calibrate Before Use: Improving Few-Shot Performance of Language Models
Tony Z. Zhao, Eric Wallace, Shi Feng +2
GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the cho…
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan +2
The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-bl…
Gradient-based Analysis of NLP Models is Manipulable
Junlin Wang, Jens Tuyls, Eric Wallace +1
Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their s…
Concealed Data Poisoning Attacks on NLP Models
Eric Wallace, Tony Z. Zhao, Shi Feng +1
Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, con…