52 citations · 155 across the 26 of their papers we have counts for
4 papers · 1 filter
Complementary Explanations for Effective In-Context Learning
Xi Ye, Srinivasan Iyer, Asli Celikyilmaz +3
Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts, but there has been limited understanding of exactly how these explanat…
Assessing Out-of-Domain Language Model Performance from Few Examples
Prasann Singhal, Jarad Forristal, Xi Ye +1
While pretrained language models have exhibited impressive generalization capabilities, they still behave unpredictably under certain domain shifts. In particular, a model may lear…
Diagnosing Ensemble Few-Shot Classifiers
Weikai Yang, Xi Ye, Xingxing Zhang +6
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually diffi…
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning
Xi Ye, Greg Durrett
Does prompting a large language model (LLM) like GPT-3 with explanations improve in-context learning? We study this question on two NLP tasks that involve reasoning over text, name…