263 citations · 694 across the 35 of their papers we have counts for
7 papers · 1 filter
Data Curation Alone Can Stabilize In-context Learning
Ting-Yun Chang, Robin Jia
In-context learning (ICL) enables large language models (LLMs) to perform new tasks by prompting them with a sequence of training examples. However, it is known that ICL is very se…
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models
Albert Xu, Xiang Ren, Robin Jia
In many task settings, text classification models are likely to encounter examples from novel classes on which they cannot predict correctly. Selective prediction, in which models…
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems
Wang Zhu, Jesse Thomason, Robin Jia
For vision-and-language reasoning tasks, both fully connectionist, end-to-end methods and hybrid, neuro-symbolic methods have achieved high in-distribution performance. In which ou…
Benchmarking Long-tail Generalization with Likelihood Splits
Ameya Godbole, Robin Jia
In order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances. We propose a method to create challenging benchmarks that require ge…
Are Sample-Efficient NLP Models More Robust?
Nelson F. Liu, Ananya Kumar, Percy Liang +1
Recent results in image classification and extractive question answering have observed that pre-trained models trained on less in-distribution data have better out-of-distribution…
On Continual Model Refinement in Out-of-Distribution Data Streams
Bill Yuchen Lin, Sida Wang, Xi Victoria Lin +4
Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrop…