AI Open最新文献_第2页

GPT understands, too GPT 也理解

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2023.08.012

引用次数: 0

Authorship style transfer with inverse transfer data augmentation 作者风格转移与反向转移数据增强

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.08.003

Zhonghui Shao , Jing Zhang , Haoyang Li , Xinmei Huang , Chao Zhou , Yuanchun Wang , Jibing Gong , Cuiping Li , Hong Chen

{"title":"Authorship style transfer with inverse transfer data augmentation","authors":"Zhonghui Shao , Jing Zhang , Haoyang Li , Xinmei Huang , Chao Zhou , Yuanchun Wang , Jibing Gong , Cuiping Li , Hong Chen","doi":"10.1016/j.aiopen.2024.08.003","DOIUrl":"10.1016/j.aiopen.2024.08.003","url":null,"abstract":"<div><p>Authorship style transfer aims to modify the style of neutral text to match the unique speaking or writing style of a particular individual. While Large Language Models (LLMs) present promising solutions, their effectiveness is limited by the small number of in-context learning demonstrations, particularly for authorship styles not frequently seen during pre-training. In response, this paper proposes an inverse transfer data augmentation (<span>ITDA</span>) method, leveraging LLMs to create (neutral text, stylized text) pairs. This method involves removing the existing styles from stylized texts, a process made more feasible due to the prevalence of neutral texts in pre-training. We use this augmented dataset to train a compact model that is efficient for deployment and adept at replicating the targeted style. Our experimental results, conducted across four datasets with distinct authorship styles, establish the effectiveness of <span>ITDA</span> over traditional style transfer methods and forward transfer using GPT-3.5. For further research and application, our dataset and code are openly accessible at <span><span>https://github.com/Vicky-Shao/ITDA</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 94-103"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651024000135/pdfft?md5=3a5bc730b200d5992d33b797c1afbf4f&pid=1-s2.0-S2666651024000135-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142075773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large language models in law: A survey 法律中的大型语言模型：调查

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.09.002

Jinqi Lai , Wensheng Gan , Jiayang Wu , Zhenlian Qi , Philip S. Yu

{"title":"Large language models in law: A survey","authors":"Jinqi Lai , Wensheng Gan , Jiayang Wu , Zhenlian Qi , Philip S. Yu","doi":"10.1016/j.aiopen.2024.09.002","DOIUrl":"10.1016/j.aiopen.2024.09.002","url":null,"abstract":"<div><div>The advent of artificial intelligence (AI) has significantly impacted the traditional judicial industry. Moreover, recently, with the development of AI-generated content (AIGC), AI and law have found applications in various domains, including image recognition, automatic text generation, and interactive chat. With the rapid emergence and growing popularity of large models, it is evident that AI will drive transformation in the traditional judicial industry. However, the application of legal large language models (LLMs) is still in its nascent stage. Several challenges need to be addressed. In this paper, we aim to provide a comprehensive survey of legal LLMs. We not only conduct an extensive survey of LLMs but also expose their applications in the judicial system. We first provide an overview of AI technologies in the legal field and showcase the recent research in LLMs. Then, we discuss the practical implementations presented by legal LLMs, such as providing legal advice to users and assisting judges during trials. In addition, we explore the limitations of legal LLMs, including data, algorithms, and judicial practice. Finally, we summarize practical recommendations and propose future development directions to address these challenges.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 181-196"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations 深度强化学习算法对对抗性扰动的自然鲁棒性研究

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.08.005

Qisai Liu , Xian Yeow Lee , Soumik Sarkar

{"title":"A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations","authors":"Qisai Liu , Xian Yeow Lee , Soumik Sarkar","doi":"10.1016/j.aiopen.2024.08.005","DOIUrl":"10.1016/j.aiopen.2024.08.005","url":null,"abstract":"<div><p>Deep reinforcement learning (DRL) has been shown to have numerous potential applications in the real world. However, DRL algorithms are still extremely sensitive to noise and adversarial perturbations, hence inhibiting the deployment of RL in many real-life applications. Analyzing the robustness of DRL algorithms to adversarial attacks is an important prerequisite to enabling the widespread adoption of DRL algorithms. Common perturbations on DRL frameworks during test time include perturbations to the observation and the action channel. Compared with observation channel attacks, action channel attacks are less studied; hence, few comparisons exist that compare the effectiveness of these attacks in DRL literature. In this work, we examined the effectiveness of these two paradigms of attacks on common DRL algorithms and studied the natural robustness of DRL algorithms towards various adversarial attacks in hopes of gaining insights into the individual response of each type of algorithm under different attack conditions.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 126-141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651024000159/pdfft?md5=a50110d80c809055a00e87466dc649b1&pid=1-s2.0-S2666651024000159-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CellBoost: A pipeline for machine assisted annotation in neuroanatomy CellBoost：神经解剖学中的机器辅助注释管道

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.09.001

Kui Qian , Beth Friedman , Jun Takatoh , Alexander Groisman , Fan Wang , David Kleinfeld , Yoav Freund

{"title":"CellBoost: A pipeline for machine assisted annotation in neuroanatomy","authors":"Kui Qian , Beth Friedman , Jun Takatoh , Alexander Groisman , Fan Wang , David Kleinfeld , Yoav Freund","doi":"10.1016/j.aiopen.2024.09.001","DOIUrl":"10.1016/j.aiopen.2024.09.001","url":null,"abstract":"<div><p>One of the important yet labor intensive tasks in neuroanatomy is the identification of select populations of cells. Current high-throughput techniques enable marking cells with histochemical fluorescent molecules as well as through the genetic expression of fluorescent proteins. Modern scanning microscopes allow high resolution multi-channel imaging of the mechanically or optically sectioned brain with thousands of marked cells per square millimeter. Manual identification of all marked cells is prohibitively time consuming. At the same time, simple segmentation algorithms to identify marked cells suffer from high error rates and sensitivity to variation in fluorescent intensity and spatial distribution.</p><p>We present a methodology that combines human judgement and machine learning that serves to significantly reduce the labor of the anatomist while improving the consistency of the annotation.</p><p>As a demonstration, we analyzed murine brains with marked premotor neurons in the brainstem. We compared the error rate of our method to the disagreement rate among human anatomists. This comparison shows that our method can reduce the time to annotate by as much as ten-fold without significantly increasing the rate of errors. We show that our method achieves significant reduction in labor while achieving an accuracy that is similar to the level of agreement between different anatomists.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 142-154"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651024000160/pdfft?md5=d645a8a10e8ed886c8fad283100f34b8&pid=1-s2.0-S2666651024000160-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relation-aware deep neural network enables more efficient biomedical knowledge acquisition from massive literature 关系感知深度神经网络让从海量文献中获取生物医学知识更高效

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.08.002

Chenyang Song , Zheni Zeng , Changyao Tian , Kuai Li , Yuan Yao , Suncong Zheng , Zhiyuan Liu , Maosong Sun

{"title":"Relation-aware deep neural network enables more efficient biomedical knowledge acquisition from massive literature","authors":"Chenyang Song , Zheni Zeng , Changyao Tian , Kuai Li , Yuan Yao , Suncong Zheng , Zhiyuan Liu , Maosong Sun","doi":"10.1016/j.aiopen.2024.08.002","DOIUrl":"10.1016/j.aiopen.2024.08.002","url":null,"abstract":"<div><p>Biomedical knowledge is typically organized in a relational scheme, such as chemical-disease relation, gene-disease relation, and gene-pathway relation. Biomedical scientists heavily rely on search engines to acquire up-to-date relational knowledge from massive biomedical articles. The navigation efficiency of the retrieval process, however, is significantly restricted by keyword matching techniques unaware of the biomedical relations of these keywords in articles. To bridge the gap between existing retrieval techniques and practical access demands for relational knowledge, we present a novel framework, <strong>Bio</strong>medical <strong>R</strong>elation-<strong>A</strong>ware <strong>D</strong>ocument <strong>R</strong>anking (BioRADR), capable of retrieving articles expressing specific relations with respect to the queried entity pair. Based on a deep neural network, BioRADR can be trained from large-scale data automatically annotated via distant supervision, and empirical evaluation reveals that it outperforms the strongest baseline by over 8 points in NDCG@1. We implement an online system (<span><span>http://bioradr.ai.thunlp.org/</span><svg><path></path></svg></span>) based on BioRADR, enabling more efficient relation-oriented retrieval of biomedical articles.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 104-114"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651024000123/pdfft?md5=0371d6da4f7cdd9c7adbcc0dac99a13d&pid=1-s2.0-S2666651024000123-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Label-aware debiased causal reasoning for Natural Language Inference 用于自然语言推理的标签感知去标签化因果推理

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.02.001

Kun Zhang , Dacao Zhang , Le Wu , Richang Hong , Ye Zhao , Meng Wang

{"title":"Label-aware debiased causal reasoning for Natural Language Inference","authors":"Kun Zhang , Dacao Zhang , Le Wu , Richang Hong , Ye Zhao , Meng Wang","doi":"10.1016/j.aiopen.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.aiopen.2024.02.001","url":null,"abstract":"<div><p>Recently, researchers have argued that the impressive performance of Natural Language Inference (NLI) models is highly due to the <em>spurious correlations</em> existing in training data, which makes models vulnerable and poorly generalized. Some work has made preliminary debiased attempts by developing data-driven interventions or model-level debiased learning. Despite the progress, existing debiased methods either suffered from the high cost of data annotation processing, or required elaborate design to identify biased factors. By conducting detailed investigations and data analysis, we argue that label information can provide meaningful guidance to identify these spurious correlations in training data, which has not been paid enough attention. Thus, we design a novel <em>Label-aware Debiased Causal Reasoning Network</em> (<em>LDCRN</em>). Specifically, according to the data analysis, we first build a causal graph to describe causal relations and spurious correlations in NLI. Then, we employ an NLI model (e.g., RoBERTa) to calculate total causal effect of input sentences to labels. Meanwhile, we design a novel label-aware biased module to model spurious correlations and calculate their causal effect in a fine-grained manner. The debiasing process is realized by subtracting this causal effect from total causal effect. Finally, extensive experiments over two well-known NLI datasets and multiple human-annotated challenging test sets are conducted to prove the superiority of <em>LDCRN</em>. Moreover, we have developed novel challenging test sets based on MultiNLI to facilitate the community.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 70-78"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651024000081/pdfft?md5=1863010d7dc5353ee714fa3b391ab574&pid=1-s2.0-S2666651024000081-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140138581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting graph search with attention network for solving the general orienteering problem 利用注意力网络促进图搜索，解决一般定向问题

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.01.006

Zongtao Liu , Wei Dong , Chaoliang Wang , Haoqingzi Shen , Gang Sun , Qun jiang , Quanjin Tao , Yang Yang

{"title":"Boosting graph search with attention network for solving the general orienteering problem","authors":"Zongtao Liu , Wei Dong , Chaoliang Wang , Haoqingzi Shen , Gang Sun , Qun jiang , Quanjin Tao , Yang Yang","doi":"10.1016/j.aiopen.2024.01.006","DOIUrl":"https://doi.org/10.1016/j.aiopen.2024.01.006","url":null,"abstract":"<div><p>Recently, several studies explore to use neural networks(NNs) to solve different routing problems, which is an auspicious direction. These studies usually design an encoder–decoder based framework that uses encoder embeddings of nodes and the problem-specific context to iteratively generate node sequence(path), and further optimize the produced result on top, such as a beam search. However, these models are limited to accepting only the coordinates of nodes as input, disregarding the self-referential nature of the studied routing problems, and failing to account for the low reliability of node selection in the initial stages, thereby posing challenges for real-world applications.</p><p>In this paper, we take the orienteering problem as an example to tackle these limitations in the previous studies. We propose a novel combination of a variant beam search algorithm and a learned heuristic for solving the general orienteering problem. We acquire the heuristic with an attention network that takes the distances among nodes as input, and learn it via a reinforcement learning framework. The empirical studies show that our method can surpass a wide range of baselines and achieve results iteratively generate the optimal or highly specialized approach.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 46-54"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266665102400007X/pdfft?md5=4bd44cc9b0d6326c8e34b456fa017774&pid=1-s2.0-S266665102400007X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139936464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves Wave2Graph：整合频谱特征和相关性，实现基于图谱的声波学习

AI Open Pub Date : 2024-01-01 DOI: 10.1016/j.aiopen.2024.08.004

Van-Truong Hoang , Khanh-Tung Tran , Xuan-Son Vu , Duy-Khuong Nguyen , Monowar Bhuyan , Hoang D. Nguyen

{"title":"Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves","authors":"Van-Truong Hoang , Khanh-Tung Tran , Xuan-Son Vu , Duy-Khuong Nguyen , Monowar Bhuyan , Hoang D. Nguyen","doi":"10.1016/j.aiopen.2024.08.004","DOIUrl":"10.1016/j.aiopen.2024.08.004","url":null,"abstract":"<div><p>This paper investigates a novel graph-based representation of sound waves inspired by the physical phenomenon of correlated vibrations. We propose a Wave2Graph framework for integrating multiple acoustic representations, including the spectrum of frequencies and correlations, into various neural computing architectures to achieve new state-of-the-art performances in sound classification. The capability and reliability of our end-to-end framework are evidently demonstrated in voice pathology for low-cost and non-invasive mass-screening of medical conditions, including respiratory illnesses and Alzheimer’s Dementia. We conduct extensive experiments on multiple public benchmark datasets (ICBHI and ADReSSo) and our real-world dataset (IJSound: Respiratory disease detection using coughs and breaths). Wave2Graph framework consistently outperforms previous state-of-the-art methods with a large magnitude, up to 7.65% improvement, promising the usefulness of graph-based representation in signal processing and machine learning.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 115-125"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651024000147/pdfft?md5=39354e1c8fc8f37b3f91eb3d652b379f&pid=1-s2.0-S2666651024000147-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How to generate popular post headlines on social media? 如何在社交媒体上生成受欢迎的帖子标题？

AI Open Pub Date : 2023-12-16 DOI: 10.1016/j.aiopen.2023.12.002

Zhouxiang Fang , Min Yu , Zhendong Fu , Boning Zhang , Xuanwen Huang , Xiaoqi Tang , Yang Yang

{"title":"How to generate popular post headlines on social media?","authors":"Zhouxiang Fang , Min Yu , Zhendong Fu , Boning Zhang , Xuanwen Huang , Xiaoqi Tang , Yang Yang","doi":"10.1016/j.aiopen.2023.12.002","DOIUrl":"https://doi.org/10.1016/j.aiopen.2023.12.002","url":null,"abstract":"<div><p>Posts, as important containers of user-generated-content on social media, are of tremendous social influence and commercial value. As an integral component of post, headline has decisive influence on post’s popularity. However, the current mainstream method for headline generation is still manually writing, which is unstable and requires extensive human effort. This drives us to explore a novel research question: Can we automate the generation of popular headlines on social media? We collect more than 1 million posts of 42,447 thousand celebrities from public data of Xiaohongshu, which is a well-known social media platform in China. We then conduct careful observations on the headlines of these posts. Observation results demonstrate that trends and personal styles are widespread in headlines on social medias and have significant contribution to posts’ popularity. Motivated by these insights, we present MEBART, which combines <strong>M</strong>ultiple preference-<strong>E</strong>xtractors with <strong>B</strong>idirectional and <strong>A</strong>uto-Regressive Transformers (BART), capturing trends and personal styles to generate popular headlines on social medias. We perform extensive experiments on real-world datasets and achieve SOTA performance compared with advanced baselines. In addition, ablation and case studies demonstrate that <em>MEBART</em> advances in capturing trends and personal styles.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"5 ","pages":"Pages 1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651023000244/pdfft?md5=77f6189a8605961caeb7262aab78dbf9&pid=1-s2.0-S2666651023000244-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0