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20162024
most citedEnd-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

42 citations · 63 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CL20242 cited

Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models

Yunhong He, Jianling Qiu, Wei Zhang +1

Recent advancements in large language models (LLMs) have significantly enhanced capabilities in natural language processing and artificial intelligence. These models, including GPT…

cs.CL20237 cited

Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models

Kaiyuan Gao, Sunan He, Zhenyu He +4

Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend thei…

cs.CL20221 cited

Effective Few-Shot Named Entity Linking by Meta-Learning

Xiuxing Li, Zhenyu Li, Zhengyan Zhang +5

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g.,…

cs.CL201810 cited

Adversarial Learning for Chinese NER from Crowd Annotations

YaoSheng Yang, Meishan Zhang, Wenliang Chen +3

To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be o…

cs.CL201642 cited

End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

Yang Yu, Wei Zhang, Kazi Hasan +3

This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given docum…