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Found 7 papers, 5 papers with code
中国语言学研究 70 年:核心期刊的词汇增长(70 Years of Linguistics Research in China: Vocabulary Growth of Core Journals)
“建国以来我国语言学经过 70 年的发展取得了瞩目的成就, 已有研究主要以回顾主要历史事件的方式介绍这一进程, 但尚缺少使用量化手段分析其历时发展的研究。本文以词汇增长为切入点探究这一主题, 首次创建大规模语言学中文核心期刊摘要的历时语料库, 并使用三大词汇增长模型预测语料库中词汇的变化。本文选择拟合效果最好的 Heaps 模型分阶段深入分析语言学词汇的变化, 显示出国家政策的指导作用和特定时代的语言生活特征。此外, 与时序无关的验证程序支撑了本文研究方法的有效性。 关键词:中国语言学;词汇增长;核心期刊;摘要;语料库;历时发展”
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model
Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale.
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research.
Human-in-the-loop Machine Translation with Large Language Model
The large language model (LLM) has garnered significant attention due to its in-context learning mechanisms and emergent capabilities.
Variance-Aware Machine Translation Test Sets
We release 70 small and discriminative test sets for machine translation (MT) evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions.
Difficulty-Aware Machine Translation Evaluation
The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation.
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT).
Domain Adaptation
Low-Resource Neural Machine Translation
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