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Found 7 papers, 2 papers with code
基于义原表示学习的词向量表示方法(Word Representation based on Sememe Representation Learning)
“本文利用知网(HowNet)中的知识, 并将Word2vec模型的结构和思想迁移至义原表示学习过程中, 提出了一个基于义原表示学习的词向量表示方法。首先, 本文利用OpenHowNet获取义原知识库中的所有义原、所有中文词汇以及所有中文词汇和其对应的义原集合, 作为实验的数据集。然后, 基于Skip-gram模型, 训练义原表示学习模型, 进而获得词向量。最后, 通过词相似度任务、词义消歧任务、词汇类比和观察最近邻义原, 来评价本文提出的方法获取的词向量的效果。通过和基线模型比较, 发现本文提出的方法既高效又准确, 不依赖大规模语料也不需要复杂的网络结构和繁多的参数, 也能提升各种自然语言处理任务的准确率。”
An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration
While Large Language Models (LLMs) demonstrate exceptional performance in a multitude of Natural Language Processing (NLP) tasks, they encounter challenges in practical applications, including issues with hallucinations, inadequate knowledge updating, and limited transparency in the reasoning process.
LLsM: Generative Linguistic Steganography with Large Language Model
Regarding open-source LLMs, we reconstruct the token generator of LLM to the "stego generator" so that it can control the generation of stego based on the secret.
UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis
In this paper, we propose the UP4LS, a novel framework with the User Profile for enhancing LS performance.
A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation
And 77. 58% success rate is obtained in the transfer test attack.
P$^2$-GAN: Efficient Style Transfer Using Single Style Image
In this paper, a novel Patch Permutation GAN (P$^2$-GAN) network that can efficiently learn the stroke style from a single style image is proposed.
Invisible Steganography via Generative Adversarial Networks
Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms.
