ArXiv最新文献_第8页

Performative Reinforcement Learning in Gradually Shifting Environments 渐变环境中的表演强化学习

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09838

Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic

{"title":"Performative Reinforcement Learning in Gradually Shifting Environments","authors":"Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic","doi":"10.48550/arXiv.2402.09838","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09838","url":null,"abstract":"When Reinforcement Learning (RL) agents are deployed in practice, they might impact their environment and change its dynamics. Ongoing research attempts to formally model this phenomenon and to analyze learning algorithms in these models. To this end, we propose a framework where the current environment depends on the deployed policy as well as its previous dynamics. This is a generalization of Performative RL (PRL) [Mandal et al., 2023]. Unlike PRL, our framework allows to model scenarios where the environment gradually adjusts to a deployed policy. We adapt two algorithms from the performative prediction literature to our setting and propose a novel algorithm called Mixed Delayed Repeated Retraining (MDRR). We provide conditions under which these algorithms converge and compare them using three metrics: number of retrainings, approximation guarantee, and number of samples per deployment. Unlike previous approaches, MDRR combines samples from multiple deployments in its training. This makes MDRR particularly suitable for scenarios where the environment's response strongly depends on its previous dynamics, which are common in practice. We experimentally compare the algorithms using a simulation-based testbed and our results show that MDRR converges significantly faster than previous approaches.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"11 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963741","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

Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence 生成式人工智能时代大型语言模型基准的不足之处

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09880

Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, M. Halgamuge

{"title":"Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence","authors":"Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, M. Halgamuge","doi":"10.48550/arXiv.2402.09880","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09880","url":null,"abstract":"The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their LLM benchmarks. Noticing preliminary inadequacies in those benchmarks, we embarked on a study to critically assess 23 state-of-the-art LLM benchmarks, using our novel unified evaluation framework through the lenses of people, process, and technology, under the pillars of functionality and security. Our research uncovered significant limitations, including biases, difficulties in measuring genuine reasoning, adaptability, implementation inconsistencies, prompt engineering complexity, evaluator diversity, and the overlooking of cultural and ideological norms in one comprehensive assessment. Our discussions emphasized the urgent need for standardized methodologies, regulatory certainties, and ethical guidelines in light of Artificial Intelligence (AI) advancements, including advocating for an evolution from static benchmarks to dynamic behavioral profiling to accurately capture LLMs' complex behaviors and potential risks. Our study highlighted the necessity for a paradigm shift in LLM evaluation methodologies, underlining the importance of collaborative efforts for the development of universally accepted benchmarks and the enhancement of AI systems' integration into society.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963773","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

Why are Sensitive Functions Hard for Transformers? 为什么变压器难以实现敏感功能？

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09963

Michael Hahn, Mark Rofin

引用次数: 0

Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence 解锁结构测量：介绍 PDD--位置话语一致性的自动度量标准

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10175

Yinhong Liu, Yixuan Su, Ehsan Shareghi, Nigel Collier

引用次数: 0

Construction of CCC and ZCCS Through Additive Characters Over Galois Field 通过伽罗瓦场上的加法字符构建 CCC 和 ZCCS

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09757

Gobinda Ghosh, S. Majhi, Subhabrata Paul

引用次数: 0

Parameterized Algorithms for Steiner Forest in Bounded Width Graphs 有界宽度图中斯坦纳森林的参数化算法

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09835

A. Feldmann, M. Lampis

{"title":"Parameterized Algorithms for Steiner Forest in Bounded Width Graphs","authors":"A. Feldmann, M. Lampis","doi":"10.48550/arXiv.2402.09835","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09835","url":null,"abstract":"In this paper we reassess the parameterized complexity and approximability of the well-studied Steiner Forest problem in several graph classes of bounded width. The problem takes an edge-weighted graph and pairs of vertices as input, and the aim is to find a minimum cost subgraph in which each given vertex pair lies in the same connected component. It is known that this problem is APX-hard in general, and NP-hard on graphs of treewidth 3, treedepth 4, and feedback vertex set size 2. However, Bateni, Hajiaghayi and Marx [JACM, 2011] gave an approximation scheme with a runtime of $n^{O(frac{k^2}{varepsilon})}$ on graphs of treewidth $k$. Our main result is a much faster efficient parameterized approximation scheme (EPAS) with a runtime of $2^{O(frac{k^2}{varepsilon} log frac{k^2}{varepsilon})} cdot n^{O(1)}$. If $k$ instead is the vertex cover number of the input graph, we show how to compute the optimum solution in $2^{O(k log k)} cdot n^{O(1)}$ time, and we also prove that this runtime dependence on $k$ is asymptotically best possible, under ETH. Furthermore, if $k$ is the size of a feedback edge set, then we obtain a faster $2^{O(k)} cdot n^{O(1)}$ time algorithm, which again cannot be improved under ETH.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"12 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962706","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

DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization 梦想捕手针对语义一致的文本到图像个性化的外观匹配自我关注

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09812

Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin, Seungryong Kim, Seunggyu Chang

{"title":"DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization","authors":"Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin, Seungryong Kim, Seunggyu Chang","doi":"10.48550/arXiv.2402.09812","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09812","url":null,"abstract":"The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept, generating diverse images of the concept aligned with the target prompts. Conventional methods representing the reference concepts using unique text embeddings often fail to accurately mimic the appearance of the reference. To address this, one solution may be explicitly conditioning the reference images into the target denoising process, known as key-value replacement. However, prior works are constrained to local editing since they disrupt the structure path of the pre-trained T2I model. To overcome this, we propose a novel plug-in method, called DreamMatcher, which reformulates T2I personalization as semantic matching. Specifically, DreamMatcher replaces the target values with reference values aligned by semantic matching, while leaving the structure path unchanged to preserve the versatile capability of pre-trained T2I models for generating diverse structures. We also introduce a semantic-consistent masking strategy to isolate the personalized concept from irrelevant regions introduced by the target prompts. Compatible with existing T2I models, DreamMatcher shows significant improvements in complex scenarios. Intensive analyses demonstrate the effectiveness of our approach.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"7 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962723","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

TSTEM: A Cognitive Platform for Collecting Cyber Threat Intelligence in the Wild TSTEM：在野外收集网络威胁情报的认知平台

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09973

Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh, A. Mahmoodi, Justin Seby, Panos Kostakos

{"title":"TSTEM: A Cognitive Platform for Collecting Cyber Threat Intelligence in the Wild","authors":"Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh, A. Mahmoodi, Justin Seby, Panos Kostakos","doi":"10.48550/arXiv.2402.09973","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09973","url":null,"abstract":"The extraction of cyber threat intelligence (CTI) from open sources is a rapidly expanding defensive strategy that enhances the resilience of both Information Technology (IT) and Operational Technology (OT) environments against large-scale cyber-attacks. While previous research has focused on improving individual components of the extraction process, the community lacks open-source platforms for deploying streaming CTI data pipelines in the wild. To address this gap, the study describes the implementation of an efficient and well-performing platform capable of processing compute-intensive data pipelines based on the cloud computing paradigm for real-time detection, collecting, and sharing CTI from different online sources. We developed a prototype platform (TSTEM), a containerized microservice architecture that uses Tweepy, Scrapy, Terraform, ELK, Kafka, and MLOps to autonomously search, extract, and index IOCs in the wild. Moreover, the provisioning, monitoring, and management of the TSTEM platform are achieved through infrastructure as a code (IaC). Custom focus crawlers collect web content, which is then processed by a first-level classifier to identify potential indicators of compromise (IOCs). If deemed relevant, the content advances to a second level of extraction for further examination. Throughout this process, state-of-the-art NLP models are utilized for classification and entity extraction, enhancing the overall IOC extraction methodology. Our experimental results indicate that these models exhibit high accuracy (exceeding 98%) in the classification and extraction tasks, achieving this performance within a time frame of less than a minute. The effectiveness of our system can be attributed to a finely-tuned IOC extraction method that operates at multiple stages, ensuring precise identification of relevant information with low false positives.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"6 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962731","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

LLM-based Federated Recommendation 基于 LLM 的联合推荐

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09959

Jujia Zhao, Wenjie Wang, Chen Xu, Zhaochun Ren, See-kiong Ng, Tat-seng Chua

{"title":"LLM-based Federated Recommendation","authors":"Jujia Zhao, Wenjie Wang, Chen Xu, Zhaochun Ren, See-kiong Ng, Tat-seng Chua","doi":"10.48550/arXiv.2402.09959","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09959","url":null,"abstract":"Large Language Models (LLMs), with their advanced contextual understanding abilities, have demonstrated considerable potential in enhancing recommendation systems via fine-tuning methods. However, fine-tuning requires users' behavior data, which poses considerable privacy risks due to the incorporation of sensitive user information. The unintended disclosure of such data could infringe upon data protection laws and give rise to ethical issues. To mitigate these privacy issues, Federated Learning for Recommendation (Fed4Rec) has emerged as a promising approach. Nevertheless, applying Fed4Rec to LLM-based recommendation presents two main challenges: first, an increase in the imbalance of performance across clients, affecting the system's efficiency over time, and second, a high demand on clients' computational and storage resources for local training and inference of LLMs. To address these challenges, we introduce a Privacy-Preserving LLM-based Recommendation (PPLR) framework. The PPLR framework employs two primary strategies. First, it implements a dynamic balance strategy, which involves the design of dynamic parameter aggregation and adjustment of learning speed for different clients during the training phase, to ensure relatively balanced performance across all clients. Second, PPLR adopts a flexible storage strategy, selectively retaining certain sensitive layers of the language model on the client side while offloading non-sensitive layers to the server. This approach aims to preserve user privacy while efficiently saving computational and storage resources. Experimental results demonstrate that PPLR not only achieves a balanced performance among clients but also enhances overall system performance in a manner that is both computationally and storage-efficient, while effectively protecting user privacy.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962743","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

Recovering the Pre-Fine-Tuning Weights of Generative Models 恢复生成模型的预微调权重

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10208

Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen

引用次数: 0