ArXiv Pub Date : 2024-03-11 DOI: 10.1145/3589335.3651245 10.1145/3589335.3651245 10.1145/3589335.3651245

Huahang Li, Shuangyin Li, Fei Hao, C. Zhang, Yuanfeng Song, Lei Chen

{"title":"BoostER: Leveraging Large Language Models for Enhancing Entity Resolution","authors":"Huahang Li, Shuangyin Li, Fei Hao, C. Zhang, Yuanfeng Song, Lei Chen","doi":"10.1145/3589335.3651245 10.1145/3589335.3651245 10.1145/3589335.3651245","DOIUrl":"https://doi.org/10.1145/3589335.3651245 10.1145/3589335.3651245 10.1145/3589335.3651245","url":null,"abstract":"Entity resolution, which involves identifying and merging records that refer to the same real-world entity, is a crucial task in areas like Web data integration. This importance is underscored by the presence of numerous duplicated and multi-version data resources on the Web. However, achieving high-quality entity resolution typically demands significant effort. The advent of Large Language Models (LLMs) like GPT-4 has demonstrated advanced linguistic capabilities, which can be a new paradigm for this task. In this paper, we propose a demonstration system named BoostER that examines the possibility of leveraging LLMs in the entity resolution process, revealing advantages in both easy deployment and low cost. Our approach optimally selects a set of matching questions and poses them to LLMs for verification, then refines the distribution of entity resolution results with the response of LLMs. This offers promising prospects to achieve a high-quality entity resolution result for real-world applications, especially to individuals or small companies without the need for extensive model training or significant financial investment.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal Documents 探索用于大型非结构化法律文件分类的大型语言模型和层次框架

ArXiv Pub Date : 2024-03-11 DOI: 10.1007/978-3-031-56060-6_15

Nishchal Prasad, M. Boughanem, T. Dkaki

引用次数: 0

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems RecAI：利用大型语言模型开发新一代推荐系统

ArXiv Pub Date : 2024-03-11 DOI: 10.1145/3589335.3651242

Jianxun Lian, Yuxuan Lei, Xu Huang, Jing Yao, Wei Xu, Xing Xie

引用次数: 0

AI as a Child of Mother Earth: Regrounding Human-AI Interaction in Ecological Thinking 人工智能是地球母亲的孩子：以生态思维重新审视人类与人工智能的互动

ArXiv Pub Date : 2024-03-11 DOI: 10.1145/3613905.3644065

Chunchen Xu, Xiao Ge

引用次数: 0

Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach 大规模云系统中的知识感知警报聚合：一种混合方法

ArXiv Pub Date : 2024-03-11 DOI: 10.1145/3639477.3639745

Jinxi Kuang, Jinyang Liu, Junjie Huang, Renyi Zhong, Jiazhen Gu, Lan Yu, Rui Tan, Zengyin Yang, Michael R. Lyu

{"title":"Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach","authors":"Jinxi Kuang, Jinyang Liu, Junjie Huang, Renyi Zhong, Jiazhen Gu, Lan Yu, Rui Tan, Zengyin Yang, Michael R. Lyu","doi":"10.1145/3639477.3639745","DOIUrl":"https://doi.org/10.1145/3639477.3639745","url":null,"abstract":"Due to the scale and complexity of cloud systems, a system failure would trigger an\"alert storm\", i.e., massive correlated alerts. Although these alerts can be traced back to a few root causes, the overwhelming number makes it infeasible for manual handling. Alert aggregation is thus critical to help engineers concentrate on the root cause and facilitate failure resolution. Existing methods typically utilize semantic similarity-based methods or statistical methods to aggregate alerts. However, semantic similarity-based methods overlook the causal rationale of alerts, while statistical methods can hardly handle infrequent alerts. To tackle these limitations, we introduce leveraging external knowledge, i.e., Standard Operation Procedure (SOP) of alerts as a supplement. We propose COLA, a novel hybrid approach based on correlation mining and LLM (Large Language Model) reasoning for online alert aggregation. The correlation mining module effectively captures the temporal and spatial relations between alerts, measuring their correlations in an efficient manner. Subsequently, only uncertain pairs with low confidence are forwarded to the LLM reasoning module for detailed analysis. This hybrid design harnesses both statistical evidence for frequent alerts and the reasoning capabilities of computationally intensive LLMs, ensuring the overall efficiency of COLA in handling large volumes of alerts in practical scenarios. We evaluate COLA on three datasets collected from the production environment of a large-scale cloud platform. The experimental results show COLA achieves F1-scores from 0.901 to 0.930, outperforming state-of-the-art methods and achieving comparable efficiency. We also share our experience in deploying COLA in our real-world cloud system, Cloud X.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attacking Transformers with Feature Diversity Adversarial Perturbation 利用特征多样性逆向扰动攻击变压器

ArXiv Pub Date : 2024-03-10 DOI: 10.1609/aaai.v38i3.27947

Chenxing Gao, Hang Zhou, Junqing Yu, Yuteng Ye, Jiale Cai, Junle Wang, Wei Yang

引用次数: 0

FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation FARPLS：特征增强型机器人轨迹偏好标注系统辅助人类标注者的偏好激发

ArXiv Pub Date : 2024-03-10 DOI: 10.1145/3640543.3645145

Hanfang Lyu, Yuanchen Bai, Xin Liang, Ujaan Das, Chuhan Shi, Leiliang Gong, Yingchi Li, Mingfei Sun, Ming Ge, Xiaojuan Ma

{"title":"FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation","authors":"Hanfang Lyu, Yuanchen Bai, Xin Liang, Ujaan Das, Chuhan Shi, Leiliang Gong, Yingchi Li, Mingfei Sun, Ming Ge, Xiaojuan Ma","doi":"10.1145/3640543.3645145","DOIUrl":"https://doi.org/10.1145/3640543.3645145","url":null,"abstract":"Preference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems seldom support labelers to digest and identify critical differences between complex trajectories recorded in videos. Our formative study (N = 12) suggests that individuals may overlook non-salient task features and establish biased preference criteria during their preference elicitation process because of partial observations. In addition, they may experience mental fatigue when given many pairs to compare, causing their label quality to deteriorate. To mitigate these issues, we propose FARPLS, a Feature-Augmented Robot trajectory Preference Labeling System. FARPLS highlights potential outliers in a wide variety of task features that matter to humans and extracts the corresponding video keyframes for easy review and comparison. It also dynamically adjusts the labeling order according to users' familiarities, difficulties of the trajectory pair, and level of disagreements. At the same time, the system monitors labelers' consistency and provides feedback on labeling progress to keep labelers engaged. A between-subjects study (N = 42, 105 pairs of robot pick-and-place trajectories per person) shows that FARPLS can help users establish preference criteria more easily and notice more relevant details in the presented trajectories than the conventional interface. FARPLS also improves labeling consistency and engagement, mitigating challenges in preference elicitation without raising cognitive loads significantly","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoupled Contrastive Learning for Long-Tailed Recognition 针对长尾识别的解耦对比学习

ArXiv Pub Date : 2024-03-10 DOI: 10.1609/aaai.v38i6.28459

Shiyu Xuan, Shiliang Zhang

{"title":"Decoupled Contrastive Learning for Long-Tailed Recognition","authors":"Shiyu Xuan, Shiliang Zhang","doi":"10.1609/aaai.v38i6.28459","DOIUrl":"https://doi.org/10.1609/aaai.v38i6.28459","url":null,"abstract":"Supervised Contrastive Loss (SCL) is popular in visual representation learning.\u0000 Given an anchor image, SCL pulls two types of positive samples, i.e., its augmentation and other images from the same class together, while pushes negative images apart to optimize the learned embedding. In the scenario of long-tailed recognition, where the number of samples in each class is imbalanced, treating two types of positive samples equally leads to the biased optimization for intra-category distance. In addition, similarity relationship among negative samples, that are ignored by SCL, also presents meaningful semantic cues. To improve the performance on long-tailed recognition, this paper addresses those two issues of SCL by decoupling the training objective. Specifically, it decouples two types of positives in SCL and optimizes their relations toward different objectives to alleviate the influence of the imbalanced dataset. We further propose a patch-based self distillation to transfer knowledge from head to tail classes to relieve the under-representation of tail classes. It uses patch-based features to mine shared visual patterns among different instances and leverages a self distillation procedure to transfer such knowledge. Experiments on different long-tailed classification benchmarks demonstrate the superiority of our method. For instance, it achieves the 57.7% top-1 accuracy on the ImageNet-LT dataset. Combined with the ensemble-based method, the performance can be further boosted to 59.7%, which substantially outperforms many recent works. Our code will be released.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SCORE: Self-supervised Correspondence Fine-tuning for Improved Content Representations SCORE：自我监督对应关系微调，改进内容表征

ArXiv Pub Date : 2024-03-10 DOI: 10.1109/icassp48485.2024.10448060

Amit Meghanani, Thomas Hain

引用次数: 1

LLMs Still Can't Avoid Instanceof: An Investigation Into GPT-3.5, GPT-4 and Bard's Capacity to Handle Object-Oriented Programming Assignments LLMs 仍然无法避免 Instanceof：调查 GPT-3.5、GPT-4 和 Bard 处理面向对象编程作业的能力

ArXiv Pub Date : 2024-03-10 DOI: 10.1145/3639474.3640052

Bruno Pereira Cipriano, P. Alves

{"title":"LLMs Still Can't Avoid Instanceof: An Investigation Into GPT-3.5, GPT-4 and Bard's Capacity to Handle Object-Oriented Programming Assignments","authors":"Bruno Pereira Cipriano, P. Alves","doi":"10.1145/3639474.3640052","DOIUrl":"https://doi.org/10.1145/3639474.3640052","url":null,"abstract":"Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-oriented programming (OOP), with its inherent complexity involving the identification of entities, relationships, and responsibilities, is not yet mastered by these tools. Contrary to introductory programming exercises, there exists a research gap with regard to the behavior of LLMs in OOP contexts. In this study, we experimented with three prominent LLMs - GPT-3.5, GPT-4, and Bard - to solve real-world OOP exercises used in educational settings, subsequently validating their solutions using an Automatic Assessment Tool (AAT). The findings revealed that while the models frequently achieved mostly working solutions to the exercises, they often overlooked the best practices of OOP. GPT-4 stood out as the most proficient, followed by GPT-3.5, with Bard trailing last. We advocate for a renewed emphasis on code quality when employing these models and explore the potential of pairing LLMs with AATs in pedagogical settings. In conclusion, while GPT-4 showcases promise, the deployment of these models in OOP education still mandates supervision.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

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