The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and ot...The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.展开更多
The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situati...The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.展开更多
In this study,it aims at examining the differences between humangenerated and AI-generated texts in IELTS Writing Task 2.It especially focuses on lexical resourcefulness,grammatical accuracy,and contextual appropriate...In this study,it aims at examining the differences between humangenerated and AI-generated texts in IELTS Writing Task 2.It especially focuses on lexical resourcefulness,grammatical accuracy,and contextual appropriateness.We analyzed 20 essays,including 10 human written ones by Chinese university students who have achieved an IELTS writing score ranging from 5.5 to 6.0,and 10 ChatGPT-4 Turbo-generated ones,using a mixed-methods approach,through corpus-based tools(NLTK,SpaCy,AntConc)and qualitative content analysis.Results showed that AI texts exhibited superior grammatical accuracy(0.4%–3%error rates for AI vs.20–26%for university students)but higher lexical repetition(17.2%to 23.25%for AI vs.17.68%for university students)and weaker contextual adaptability(3.33/10–3.69/10 for AI vs.3.23/10 to 4.14/10 for university students).While AI’s grammatical precision supports its utility as a corrective tool,human writers outperformed AI in lexical diversity and task-specific nuance.The findings advocate for a hybrid pedagogical model that leverages AI’s strengths in error detection while retaining human instruction for advanced lexical and contextual skills.Limitations include the small corpus and single-AI-model focus,suggesting future research with diverse datasets and longitudinal designs.展开更多
目的:在前期发现琐琐葡萄总黄酮(Flavones from Vitis vinifera L,VTF)可改善APP/PS-1双转基因(Alzheimer's disease,AD)小鼠认知功能的基础上,进一步验证“VTF是否通过重塑肠道菌群-肠-脑轴而发挥神经保护作用”,并阐明其潜在机制...目的:在前期发现琐琐葡萄总黄酮(Flavones from Vitis vinifera L,VTF)可改善APP/PS-1双转基因(Alzheimer's disease,AD)小鼠认知功能的基础上,进一步验证“VTF是否通过重塑肠道菌群-肠-脑轴而发挥神经保护作用”,并阐明其潜在机制。方法:本研究将从天然药物琐琐葡萄中提取活性成分总黄酮VTF,作用于APP/PS-1小鼠,VTF灌胃8周,通过Morris水迷宫实验评估小鼠空间学习能力;ELISA检测小鼠脑组织中IL-1β、IL-6、TNF-α因子,血清中5-HT、GABA因子的含量;苏木精-伊红(Hematoxylin and eosin staining,HE)染色法观察结肠组织形态,免疫组化法检测Occludin、Claudin、Zo-1、NLRP3蛋白表达;高通量测序检测小鼠粪便肠道菌群;结果:1)水迷宫实验结果显示:VTF低剂量组平均逃逸潜伏期缩短(P<0.05);VTF低、中剂量组有效区域运动距离,运动时间以及进入次数显著增多(P<0.01);2)结肠组织HE染色结果显示;AD模型小鼠肠粘膜膜层萎缩,隐窝丢失和绒毛断裂,而VTF干预之后小鼠上述情况均有所减轻;3)结肠组织免疫组化结果显示:VTF干预可以上调Occludin、Claudin、Zo-1等紧密连接蛋白阳性表达(P<0.01),减少NLRP3炎症相关蛋白的表达(P<0.01);4)ELISA结果:VTF各剂量组小鼠脑组织中IL-1β、IL-6、TNFα等炎症因子水平降低(P<0.01);VTF各剂量组中5-HT和GABA的含量增多(P<0.01);5)肠道菌群检测结果显示:VTF高剂量干预后的AD小鼠厚壁菌门相对丰度增多(P<0.05),与模型组小鼠相比,VTF中、高剂量组中乳杆菌科(Lactobacillaceae)相对丰度增多,VTF低剂量组中Muribaculaceae相对丰度增多,多奈哌齐组,VTF各剂量组中丹毒科(Erysipelotrichaceae)、瘤胃球菌科(Ruminococcaceae)相对丰度增多;而VTF各剂量组中毛螺菌科(Lachnospiraceae)、螺杆菌科(Helicobacteraceae),脱铁杆菌科(Deferribacteraceae)相对丰度减少。结论:这些结果表明,VTF调节肠道菌群可能具有治疗衰老过程中微生物导致脑轴和认知功能缺陷的潜力,其机制可能与改变肠道菌群组成,修复受损的肠道屏障,炎症反应和神经递质有关。因此,调节肠道微生物群可能是治疗AD相关神经疾病的一种潜在策略。展开更多
基金supported in part by the Science and Technology Innovation Program of Hunan Province under Grant 2025RC3166the National Natural Science Foundation of China under Grant 62572176the National Key R&D Program of China under Grant 2024YFF0618800.
文摘The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.
文摘The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.
基金supported by the Macao Science and Technology Development Fund(FDCT)(No.0071/2023/RIB3)Joint Research Funding Program between the Macao Science and Technology Development Fund(FDCT)and the Department of Science and Technology of Guangdong Province(FDCTGDST)(No.0003-2024-AGJ).
文摘In this study,it aims at examining the differences between humangenerated and AI-generated texts in IELTS Writing Task 2.It especially focuses on lexical resourcefulness,grammatical accuracy,and contextual appropriateness.We analyzed 20 essays,including 10 human written ones by Chinese university students who have achieved an IELTS writing score ranging from 5.5 to 6.0,and 10 ChatGPT-4 Turbo-generated ones,using a mixed-methods approach,through corpus-based tools(NLTK,SpaCy,AntConc)and qualitative content analysis.Results showed that AI texts exhibited superior grammatical accuracy(0.4%–3%error rates for AI vs.20–26%for university students)but higher lexical repetition(17.2%to 23.25%for AI vs.17.68%for university students)and weaker contextual adaptability(3.33/10–3.69/10 for AI vs.3.23/10 to 4.14/10 for university students).While AI’s grammatical precision supports its utility as a corrective tool,human writers outperformed AI in lexical diversity and task-specific nuance.The findings advocate for a hybrid pedagogical model that leverages AI’s strengths in error detection while retaining human instruction for advanced lexical and contextual skills.Limitations include the small corpus and single-AI-model focus,suggesting future research with diverse datasets and longitudinal designs.
文摘目的:在前期发现琐琐葡萄总黄酮(Flavones from Vitis vinifera L,VTF)可改善APP/PS-1双转基因(Alzheimer's disease,AD)小鼠认知功能的基础上,进一步验证“VTF是否通过重塑肠道菌群-肠-脑轴而发挥神经保护作用”,并阐明其潜在机制。方法:本研究将从天然药物琐琐葡萄中提取活性成分总黄酮VTF,作用于APP/PS-1小鼠,VTF灌胃8周,通过Morris水迷宫实验评估小鼠空间学习能力;ELISA检测小鼠脑组织中IL-1β、IL-6、TNF-α因子,血清中5-HT、GABA因子的含量;苏木精-伊红(Hematoxylin and eosin staining,HE)染色法观察结肠组织形态,免疫组化法检测Occludin、Claudin、Zo-1、NLRP3蛋白表达;高通量测序检测小鼠粪便肠道菌群;结果:1)水迷宫实验结果显示:VTF低剂量组平均逃逸潜伏期缩短(P<0.05);VTF低、中剂量组有效区域运动距离,运动时间以及进入次数显著增多(P<0.01);2)结肠组织HE染色结果显示;AD模型小鼠肠粘膜膜层萎缩,隐窝丢失和绒毛断裂,而VTF干预之后小鼠上述情况均有所减轻;3)结肠组织免疫组化结果显示:VTF干预可以上调Occludin、Claudin、Zo-1等紧密连接蛋白阳性表达(P<0.01),减少NLRP3炎症相关蛋白的表达(P<0.01);4)ELISA结果:VTF各剂量组小鼠脑组织中IL-1β、IL-6、TNFα等炎症因子水平降低(P<0.01);VTF各剂量组中5-HT和GABA的含量增多(P<0.01);5)肠道菌群检测结果显示:VTF高剂量干预后的AD小鼠厚壁菌门相对丰度增多(P<0.05),与模型组小鼠相比,VTF中、高剂量组中乳杆菌科(Lactobacillaceae)相对丰度增多,VTF低剂量组中Muribaculaceae相对丰度增多,多奈哌齐组,VTF各剂量组中丹毒科(Erysipelotrichaceae)、瘤胃球菌科(Ruminococcaceae)相对丰度增多;而VTF各剂量组中毛螺菌科(Lachnospiraceae)、螺杆菌科(Helicobacteraceae),脱铁杆菌科(Deferribacteraceae)相对丰度减少。结论:这些结果表明,VTF调节肠道菌群可能具有治疗衰老过程中微生物导致脑轴和认知功能缺陷的潜力,其机制可能与改变肠道菌群组成,修复受损的肠道屏障,炎症反应和神经递质有关。因此,调节肠道微生物群可能是治疗AD相关神经疾病的一种潜在策略。
