This paper reexamines the research on out-of-distribution (OOD) robustness in the field of
NLP. We find that the distribution shift settings in previous studies commonly lack adequate …

In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of
prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot …

Pre-trained language models (PLMs) are known to improve the generalization performance
of natural language understanding models by leveraging large amounts of data during the …

Abstract Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to leverage the
useful knowledge from a source domain to identify aspect-sentiment pairs in sentences from …

To address the scarcity of massive labeled data, cross-domain named entity recognition
(cross-domain NER) attracts increasing attention. Recent studies focus on decomposing …

Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain
using the model trained on the source domain to cope with the scarcity of labeled data …

Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of
generating answers by simultaneously referring to multiple passages. Although …

Grammatical Error Correction (GEC) systems play a vital role in assisting people with their
daily writing tasks. However, users may sometimes come across a GEC system that initially …

Few-shot named entity recognition (NER) has shown remarkable progress in identifying
entities in low-resource domains. However, few-shot NER methods still struggle with out-of …

Large language models (LLMs) have made remarkable progress in a wide range of natural
language understanding and generation tasks. However, their ability to generate …