Gotowe bibliografie tematyczne / Query Prompts

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Gotowa bibliografia na temat „Query Prompts”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Query Prompts"

Delgado-Solorzano, Cecilia, Elias Tzoc, Suzanne Rook, Christopher Vinson, and Carlos Toxtli. "A Study of LLM-Powered Student Query Support." Avances en Interacción Humano-Computadora 9, no. 1 (2024): 21–25. https://doi.org/10.47756/aihc.y9i1.141.

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In this paper, we explore the use of Large Language Models (LLMs) to help students improve their information-seeking skills while encouraging the use of references to aid library literacy efforts. This study aims to expand the reach of library support by introducing an approach that leverages the capabilities of LLMs and well-structured prompts. Our approach begins with surveying the current changes students have faced in the last two years concerning their study habits and how they search for information. We subsequently propose a multi-step system prompt, referred as prompting architecture,

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Slifkin, Lawrence, and Marilyn Vogel. "Lubrication Article Prompts Suggestion and Suggestive Query." Physics Today 52, no. 11 (1999): 82. http://dx.doi.org/10.1063/1.882889.

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Zhang, Qinye. "PromptCraft-RAG: Context-based Prompt Enhancement of Refining Query for Retrieval Augmented Generation." Applied and Computational Engineering 154, no. 1 (2025): 137–44. https://doi.org/10.54254/2755-2721/2025.tj23129.

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Retrieval-augmented generation (RAG) has become a transformative framework in Natural Language Processing (NLP). It contributes to the generation process by retrieving relevant information from external knowledge bases, thus making the responses more accurate and contextualized. Recent developments in RAG have renewed interest in optimizing RAG frameworks, such as improving the efficiency of the retrieval module, query reconstruction, refinement, ranking mechanisms, and resolution of hallucinations. However, RAG still faces significant bottlenecks, especially when it comes to understanding kno

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Jiang, Zhengbao, Frank F. Xu, Jun Araki, and Graham Neubig. "How Can We Know What Language Models Know?" Transactions of the Association for Computational Linguistics 8 (July 2020): 423–38. http://dx.doi.org/10.1162/tacl_a_00324.

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Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as “ Obama is a __ by profession”. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as “ Obama worked as a __ ” may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we

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Choi, Jaekeol. "Identifying Key Terms in Prompts for Relevance Evaluation with GPT Models." International Journal on Natural Language Computing 13, no. 2 (2024): 01–19. http://dx.doi.org/10.5121/ijnlc.2024.13201.

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Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to identify which specific terms in prompts positively or negatively impact relevance evaluation with LLMs. We employed two types of prompts: those used in previous research and generated automatically by LLMs. By compa

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Meng, Fan'an, Chaoran Cui, Hongjun Dai, and Shuai Gong. "Black-Box Test-Time Prompt Tuning for Vision-Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6099–107. https://doi.org/10.1609/aaai.v39i6.32652.

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Test-time prompt tuning (TPT) aims to adjust the vision-language models (e.g., CLIP) with learnable prompts during the inference phase. However, previous works overlooked that pre-trained models as a service (MaaS) have become a noticeable trend due to their commercial usage and potential risk of misuse. In the context of MaaS, users can only design prompts in inputs and query the black-box vision-language models through inference APIs, rendering the previous paradigm of utilizing gradient for prompt tuning is infeasible. In this paper, we propose black-box test-time prompt tuning (B²TPT), a n

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Choi, Jaekeol. "Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback." Applied Sciences 15, no. 9 (2025): 5198. https://doi.org/10.3390/app15095198.

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Evaluating query-passage relevance is a crucial task in information retrieval (IR), where the performance of large language models (LLMs) greatly depends on the quality of prompts. Current prompt optimization methods typically require multiple candidate generations or iterative refinements, resulting in significant computational overhead and limited practical applicability. In this paper, we propose a novel prompt optimization method that leverages LLM-based confusion matrix feedback to efficiently optimize prompts for the relevance evaluation task. Unlike previous approaches, our method syste

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Akram, Waseem, Yanjie Jiang, Yuxia Zhang, Haris Ali Khan, and Hui Liu. "LLM-Based Method Name Suggestion with Automatically Generated Context-Rich Prompts." Proceedings of the ACM on Software Engineering 2, FSE (2025): 779–800. https://doi.org/10.1145/3715753.

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Accurate method naming is crucial for code readability and maintainability. However, manually creating concise and meaningful names remains a significant challenge. To this end, in this paper, we propose an approach based on Large Language Model (LLMs) to suggest method names according to function descriptions. The key of the approach is ContextCraft , an automated algorithm for generating context-rich prompts for LLM that suggests the expected method names according to the prompts. For a given query (functional description), it retrieves a few best examples whose functional descriptions have

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Hu, Xiaomeng, Pin-Yu Chen, and Tsung-Yi Ho. "Token Highlighter: Inspecting and Mitigating Jailbreak Prompts for Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27330–38. https://doi.org/10.1609/aaai.v39i26.34943.

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Large Language Models (LLMs) are increasingly being integrated into services such as ChatGPT to provide responses to user queries. To mitigate potential harm and prevent misuse, there have been concerted efforts to align the LLMs with human values and legal compliance by incorporating various techniques, such as Reinforcement Learning from Human Feedback (RLHF), into the training of the LLMs. However, recent research has exposed that even aligned LLMs are susceptible to adversarial manipulations known as Jailbreak Attacks. To address this challenge, this paper proposes a method called Token Hi

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Schmidt, Douglas C., Jesse Spencer-Smith, Quchen Fu, and Jules White. "Towards a Catalog of Prompt Patterns to Enhance the Discipline of Prompt Engineering." ACM SIGAda Ada Letters 43, no. 2 (2024): 43–51. http://dx.doi.org/10.1145/3672359.3672364.

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The rapid advent of Large Language Models (LLMs), such as ChatGPT and Claude, is revolutionizing various fields, from education and healthcare to the engineering of reliable software systems. These LLMs operate through "prompts," which are natural language inputs that users employ to query and leverage the models' capabilities. Given the novelty of LLMs, the understanding of how to effectively use prompts remains largely anecdotal, based on isolated use cases. This fragmented approach limits the reliability and utility of LLMs, especially when they are applied in mission-critical software envi

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Książki na temat "Query Prompts"

Gensheimer, Maryl B. The Role of Iconographical Programs at the Baths of Caracalla. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190614782.003.0003.

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To query the sociopolitical rationale that may have prompted the emperor Caracalla to endow such a monumental bathing facility, Chapter 3 addresses the iconographical trends that mark distinctive emphases within the larger body of the Baths’ decorative program. Particular attention is paid to representations of Hercules, Bacchus, and other divinities and personifications associated with the emperor, as well as Homeric and other mythological exempla that are likewise an allusion to imperial largess. Similarly, the historical reliefs from the palaestrae and the honorific portrait statues of the

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Części książek na temat "Query Prompts"

Kouwenhoven, Jonas, Lucas Lageweg, and Benno Kruit. "Constrained LLM-Based Query Generation for Question Answering on Official Statistics." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241052.

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This research explores the development of a knowledge graph statistical question answering system for Statistics Netherlands. Aimed at efficiently retrieving single statistical values from their extensive database, which encompasses over a billion values across more than 4,000 tables, we propose a comprehensive three-component framework consisting of: (1) a data augmentation method to generate synthetic data, (2) an entity retrieval system that leverages various encoder networks along with different hard negative mining techniques for the effective retrieval of tables, measures, and dimensions

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Do, Dai, Quan Tran, Svetha Venkatesh, and Hung Le. "Large Language Model Prompting with Episodic Memory." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240953.

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Prompt optimization is essential for enhancing the performance of Large Language Models (LLMs) in a range of Natural Language Processing (NLP) tasks, particularly in scenarios of few-shot learning where training examples are incorporated directly into the prompt. Despite the growing interest in optimizing prompts with few-shot examples, existing methods for prompt optimization are often resource-intensive or perform inadequately. In this work, we propose PrOmpting with Episodic Memory (POEM), a novel prompt optimization technique that is simple, efficient, and demonstrates strong generalizatio

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Seshadri, V. "Characterizations." In The Inverse Gaussian Distribution. Oxford University PressOxford, 1994. http://dx.doi.org/10.1093/oso/9780198522430.003.0003.

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Abstract This chapter is devoted to a survey of the various characterizations of IG(m, &gt; λ.). Although we have encountered numerous properties of IG(m,&gt; λ.) in the previous chapter, not all of them characterize the family uniquely. As a starting point let us consider the Gaussian family. One of the ear liest characterizations, due to Geary (1936), is based on the independence of the sample mean and the sample variance. Geary assumed the existence of moments of all orders while Lukacs (1942) proved the result by assuming only the existence of the first two moments. Kawata and Saka

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Uden, Lorna, and Chris Beaumont. "Developing Problems / Triggers." In Technology and Problem-Based Learning. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-744-7.ch006.

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The starting point for problem-based learning is a problem statement, which is also often called a trigger since it starts the PBL case and prompts the development of learning issues. It is formulated as a problem, query or puzzle that the team has to investigate and may be presented in a number of ways, for example as a video or as a simple piece of text. In many ways, the problem statement is the key to successful PBL. If it does not stimulate the students’ interest or enable students to generate learning issues that relate closely to the desired learning outcomes, then there are likely to b

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Wang, Ying-Hong. "A Multimedia Database Supports Internet-Based English Learning." In Distributed Multimedia Databases. IGI Global, 2002. http://dx.doi.org/10.4018/978-1-930708-29-7.ch017.

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This work presents a novel English distance learning system that was developed through multimedia database and Internet technologies called English Multimedia Corpus. The system includes English articles, dialogs, and videos. A student can study English writing and reading as well as view Web browser listings to connect the Corpus server. In the system, semantic query and “Link grammar” are applied to construct the English Multimedia Corpus system. Furthermore, it promotes the query level from keyword-base and content-based query to a semantic level. The main function of this system is to quer

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Khan, Rafi Ullah, Mohib Ullah, and Bushra Shafi. "Web Search Privacy Evaluation Metrics." In Protecting User Privacy in Web Search Utilization. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6914-9.ch003.

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Privacy quantification methods are used to quantify the knowledge the adverse search engine has obtained with and without privacy protection mechanisms. Thus, these methods calculate privacy exposure. Private web search techniques are based on many methods (e.g., proxy service, query modification, query exchange, and others). This variety of techniques prompted the researchers to evaluate their work differently. This section introduces the metrics used to evaluate user privacy (protection). Moreover, this section also introduces the metrics used to evaluate the performance of privacy attacks a

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Li, Yuhong, Jiajun Lu, Qiongfeng Jiang, and Zhiyuan Zeng. "Design and Implementation of a Hybrid Architecture Big Data Platform for Catchment Water Resource Spatial Temporal Management and Control." In Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210214.

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The management and protection of catchment water resource are effective measures to promote the harmonious coexistence of human and nature, and accelerate the construction of ecological civilization. Effective storage, management, and retrieval of large spatial temporal data in catchment water resource are facing enormous challenges. At the same time, higher requirements are put forward for data concurrent access support capability and security reliability. Therefore, it is urgent to carry out research on intelligent management and control of large spatial temporal data in catchment water reso

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Bessis, Nik. "Model Architecture for a User Tailored Data Push Service in Data Grids." In Grid Technology for Maximizing Collaborative Decision Management and Support. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-364-7.ch012.

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This chapter describes a framework to support runtime service discovery for Grid applications based on service discovery queries in both push and pull modes of query execution. The framework supports six different types of trigger conditions that may prompt service replacement during run-time of grid business application, and evaluates the relevance of a set of candidate services against service discovery queries. The chapter also describes the language used to express service discovery queries and the three types of fitness measurement used to evaluate the candidate services against these que

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Dooley, James, Andrea Zisman, and George Spanoudakis. "Runtime Service Discovery for Grid Applications." In Grid and Cloud Computing. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0879-5.ch402.

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Abburu, Sunitha. "GIS Based Health Information Management through LETL, Multi Criteria Query, Analysis, Visualization." In Research Anthology on Public Health Services, Policies, and Education. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8960-1.ch019.

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For effective decision making in public health information management(HIM) system, health information availability, accessibility, prompt exchange, GIS linkage, spatiotemporal analysis of diseases is crucial. Lack of cost-effective technical support and information gaps are the main obstacles in HIM. This article defines a generic conceptual process framework for effective HIM that provides cost-effective, portable, easy to use solution. The solution incorporates GIS, Mobile technology, information management concepts, ICD-10 codes, WHO and mHealth standards. The current research is implemente

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Streszczenia konferencji na temat "Query Prompts"

Yu, Lang, Qin Chen, Jiaju Lin, and Liang He. "Black-box Prompt Tuning for Vision-Language Model as a Service." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/187.

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In the scenario of Model-as-a-Service (MaaS), pre-trained models are usually released as inference APIs. Users are allowed to query those models with manually crafted prompts. Without accessing the network structure and gradient information, it's tricky to perform continuous prompt tuning on MaaS, especially for vision-language models (VLMs) considering cross-modal interaction. In this paper, we propose a black-box prompt tuning framework for VLMs to learn task-relevant prompts without back-propagation. In particular, the vision and language prompts are jointly optimized in the intrinsic param

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Choi, Jaekeol. "Influence of Prompt Terms on Relevance Evaluation with GPTs." In 5th International Conference on NLP & Information Retrieval. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140603.

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Amplayo, Reinald Kim, Kellie Webster, Michael Collins, Dipanjan Das, and Shashi Narayan. "Query Refinement Prompts for Closed-Book Long-Form QA." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.444.

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Lemeš, Samir. "Prompt Engineering." In Artificial Intelligence in Industry 4.0: The future that comes true. Academy of Sciences and Arts of Bosnia and Herzegovina, 2024. http://dx.doi.org/10.5644/pi2024.215.08.

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Prompt engineering is the process of designing, testing, and optimizing prompts that are sent to artificial intelligence (AI), especially large language models like GPT-4. The goal is to formulate the prompts in a way that allows the AI model to provide the most relevant, accurate and useful answers. This process involves understanding how the AI model interprets and responds to different query formulations, as well as tailoring those queries for different applications. Well-designed prompts can significantly improve the performance of AI systems, making them more useful and efficient for vari

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Li, Zongyi, Jianbo Li, Yuxuan Shi, et al. "Cross-modal Generation and Alignment via Attribute-guided Prompt for Unsupervised Text-based Person Retrieval." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/116.

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Text-based Person Search aims to retrieve a specified person using a given text query. Current methods predominantly rely on paired labeled image-text data to train the cross-modality retrieval model, necessitating laborious and time-consuming labeling. In response to this challenge, we present the Cross-modal Generation and Alignment via Attribute-guided Prompt framework (GAAP) for fully unsupervised text-based person search, utilizing only unlabeled images. Our proposed GAAP framework consists of two key parts: Attribute-guided Prompt Caption Generation and Attribute-guided Cross-modal Align

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Al Amin, S. T., K. Wu, A. Mathur, and C. Koritala. "LLM-In-The-Loop: A Framework for Enhancing Drilling Data Analytics." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/223805-ms.

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Abstract Analyzing and visualizing drilling data is critical for optimizing operations and informed decision-making. As the drilling data are mostly structured data coming from downhole sensors and drilling equipments, traditional methods to analyze these data often require programming skills, causing delays and repetitive tasks for experts. On the other hand, advancement in large language models (LLMs) has enabled to chat with our internal data. Human-like or natural language can be used to extract meaningful and important information from large amount of data. With these motivations in mind,

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Martin, James Lee, M. Nur Arif Zanuri, Muthu Kumar Sockalingam, and Eric Andersen. "LLMs, Embeddings and Indexing Pipelines to Enable Natural Language Searching on Upstream Datasets." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23626-ea.

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Abstract Large Language Models (LLMs) are attracting an enormous amount of interest at the moment in many domains. Their general nature, and ability to "understand" natural language, has already stimulated multiple areas of research at our company. Here we successfully demonstrate a Natural Language querying system, which is able to search a large repository of unstructured exploration data. The system supports follow up querying on the returned results, plus automatic summarization of content. The system is integrated into our novel end-to-end data-mining platform, which continuously mines ou

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Hou, Yingqi, Yichang Shao, Zhongyi Han, and Zhirui Ye. "Construction and Application of Traffic Accident Knowledge Graph Based on LLM." In 2024 International Conference on Smart Transportation Interdisciplinary Studies. SAE International, 2025. https://doi.org/10.4271/2025-01-7139.

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<div class="section abstract"><div class="htmlview paragraph">Records of traffic accidents contain a wealth of information regarding accident causes and consequences. It provides a valuable data foundation for accident analysis. The diversity and complexity of textual data pose significant challenges in knowledge extracting. Previous research primarily relies on Natural Language Processing (NLP) to extract knowledge from texts and uses knowledge graphs (KGs) to store information in a structured way. However, the process based on NLP typically necessitates extensive annotated datase

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Chen, Shuai, Mingyi Zhang, Junge Zhang, and Kaiqi Huang. "Task-Wise Prompt Query Function for Rehearsal-Free Continual Learning." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446403.

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Wang, Xin, Kai Chen, Xingjun Ma, Zhineng Chen, Jingjing Chen, and Yu-Gang Jiang. "AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning." In MM '24: The 32nd ACM International Conference on Multimedia. ACM, 2024. http://dx.doi.org/10.1145/3664647.3681032.

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