72 citations · 90 across the 7 of their papers we have counts for
4 papers · 1 filter
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
Chenglei Si, Dan Friedman, Nitish Joshi +3
In-context learning (ICL) is an important paradigm for adapting large language models (LLMs) to new tasks, but the generalization behavior of ICL remains poorly understood. We inve…
Active Example Selection for In-Context Learning
Yiming Zhang, Shi Feng, Chenhao Tan
With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-t…
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…
Universal Adversarial Triggers for Attacking and Analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal +2
Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens…