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Revisiting out-of-distribution robustness in nlp: Benchmarks, analysis, and llms evaluations
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 …
NLP. We find that the distribution shift settings in previous studies commonly lack adequate …
Promptner: Prompting for named entity recognition
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 …
prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot …
Glue-x: Evaluating natural language understanding models from an out-of-distribution generalization perspective
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 …
of natural language understanding models by leveraging large amounts of data during the …
Cross-domain data augmentation with domain-adaptive language modeling for aspect-based sentiment analysis
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 …
useful knowledge from a source domain to identify aspect-sentiment pairs in sentences from …
Rfid: Towards rational fusion-in-decoder for open-domain question answering
Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of
generating answers by simultaneously referring to multiple passages. Although …
generating answers by simultaneously referring to multiple passages. Although …
Prompting large language models for counterfactual generation: An empirical study
Large language models (LLMs) have made remarkable progress in a wide range of natural
language understanding and generation tasks. However, their ability to generate …
language understanding and generation tasks. However, their ability to generate …
VerifiNER: verification-augmented NER via knowledge-grounded reasoning with large language models
Recent approaches in domain-specific named entity recognition (NER), such as biomedical
NER, have shown remarkable advances. However, they still lack of faithfulness, producing …
NER, have shown remarkable advances. However, they still lack of faithfulness, producing …
Mere contrastive learning for cross-domain sentiment analysis
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 …
using the model trained on the source domain to cope with the scarcity of labeled data …
Decoupled hyperbolic graph attention network for cross-domain named entity recognition
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 NER) attracts increasing attention. Recent studies focus on decomposing …
[HTML][HTML] A novel prompting method for few-shot ner via llms
Q Cheng, L Chen, Z Hu, J Tang, Q Xu, B Ning - Natural Language …, 2024 - Elsevier
In various natural language processing tasks, significant strides have been made by Large
Language Models (LLMs). Researchers leverage prompt method to conduct LLMs in …
Language Models (LLMs). Researchers leverage prompt method to conduct LLMs in …