[학술논문 2025] 저자: Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch | 인용수: 480 | 초록: The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when consideri
[arXiv 2025-01-06] 저자: Qingyao Ai, Jingtao Zhan, Yiqun Liu | 초록: The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., informati
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[학술논문 2025] 저자: Giuseppe Romeo, Daniela Conti | 인용수: 175 | 초록: Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environm
[arXiv 2023-09-10] 저자: Deguang Kong, Daniel Zhou, Zhiheng Huang | 초록: Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document conten
[arXiv 2001-08-07] 저자: Naren Ramakrishnan, Saverio Perugini | 초록: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Person
[학술논문 2025] 저자: Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch | 인용수: 480 | 초록: The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when consideri
[arXiv 2025-01-06] 저자: Qingyao Ai, Jingtao Zhan, Yiqun Liu | 초록: The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., informati
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[학술논문 2025] 저자: Giuseppe Romeo, Daniela Conti | 인용수: 175 | 초록: Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environm
[arXiv 2023-09-10] 저자: Deguang Kong, Daniel Zhou, Zhiheng Huang | 초록: Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document conten
[arXiv 2001-08-07] 저자: Naren Ramakrishnan, Saverio Perugini | 초록: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Person
[학술논문 2025] 저자: Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch | 인용수: 480 | 초록: The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when consideri
[arXiv 2025-01-06] 저자: Qingyao Ai, Jingtao Zhan, Yiqun Liu | 초록: The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., informati
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[학술논문 2025] 저자: Giuseppe Romeo, Daniela Conti | 인용수: 175 | 초록: Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environm
[arXiv 2023-09-10] 저자: Deguang Kong, Daniel Zhou, Zhiheng Huang | 초록: Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document conten
[arXiv 2001-08-07] 저자: Naren Ramakrishnan, Saverio Perugini | 초록: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Person
[학술논문 2025] 저자: Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch | 인용수: 480 | 초록: The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when consideri
[arXiv 2025-01-06] 저자: Qingyao Ai, Jingtao Zhan, Yiqun Liu | 초록: The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., informati
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[학술논문 2025] 저자: Giuseppe Romeo, Daniela Conti | 인용수: 175 | 초록: Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environm
[arXiv 2023-09-10] 저자: Deguang Kong, Daniel Zhou, Zhiheng Huang | 초록: Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document conten
[arXiv 2001-08-07] 저자: Naren Ramakrishnan, Saverio Perugini | 초록: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Person
[학술논문 2025] 저자: Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch | 인용수: 480 | 초록: The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when consideri
[arXiv 2025-01-06] 저자: Qingyao Ai, Jingtao Zhan, Yiqun Liu | 초록: The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., informati
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[학술논문 2025] 저자: Giuseppe Romeo, Daniela Conti | 인용수: 175 | 초록: Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environm
[arXiv 2023-09-10] 저자: Deguang Kong, Daniel Zhou, Zhiheng Huang | 초록: Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document conten
[arXiv 2001-08-07] 저자: Naren Ramakrishnan, Saverio Perugini | 초록: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Person
[학술논문 2025] 저자: Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch | 인용수: 480 | 초록: The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when consideri
[arXiv 2025-01-06] 저자: Qingyao Ai, Jingtao Zhan, Yiqun Liu | 초록: The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., informati
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[학술논문 2025] 저자: Giuseppe Romeo, Daniela Conti | 인용수: 175 | 초록: Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environm
[arXiv 2023-09-10] 저자: Deguang Kong, Daniel Zhou, Zhiheng Huang | 초록: Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document conten
[arXiv 2001-08-07] 저자: Naren Ramakrishnan, Saverio Perugini | 초록: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Person
[학술논문 2025] 저자: Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch | 인용수: 480 | 초록: The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when consideri
[arXiv 2025-01-06] 저자: Qingyao Ai, Jingtao Zhan, Yiqun Liu | 초록: The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., informati
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[학술논문 2025] 저자: Giuseppe Romeo, Daniela Conti | 인용수: 175 | 초록: Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environm
[arXiv 2023-09-10] 저자: Deguang Kong, Daniel Zhou, Zhiheng Huang | 초록: Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document conten
[arXiv 2001-08-07] 저자: Naren Ramakrishnan, Saverio Perugini | 초록: Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Person
[학술논문 2026] 저자: Wayne Xin Zhao, Kun Zhou, Junyi Li | 인용수: 1540 | 초록: Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancement
[학술논문 2024] 저자: Derong Xu, Wei Chen, Wenjun Peng | 인용수: 287 | 초록: Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE ta
[학술논문 2025] 저자: Yutao Zhu, Huaying Yuan, Shuting Wang | 인용수: 194 | 초록: As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing c
[arXiv 2025-04-20] 저자: Katelyn Xiaoying Mei, Nic Weber | 초록: The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of cri
[arXiv 2025-04-23] 저자: Xuyang Zhu, Sejoon Chang, Andrew Kuik | 초록: Retrieval-Augmented Generation (RAG) systems offer a powerful approach to enhancing large language model (LLM) outputs by incorporating fact-checked, contextually relevant information. However, fairness and reliability concerns persist, as hallucinations can emerge at both the retrieval and generation stages, affecting users' reasoning and decision-making. Our research explores how tailored warning messages -- whose content depen