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This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment.
Language models are few-shot learners
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Cutting down on prompts and parameters: Simple few-shot learning with language models
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts . 1–7
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Chain-of-thought prompting elicits reasoning in large language models
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The imperative for regulatory oversight of large language models (or generative AI) in healthcare
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Memgpt: Towards llms as operating systems
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Is chatgpt a general-purpose natural language processing task solver?
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Large language models are competitive near cold-start recommenders for language-and item-based preferences. In Proceedings of the 17th ACM conference on recommender systems . 890–896
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Arkadeep Acharya, Brijraj Singh, and Naoyuki Onoe. 2023 · 2023
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Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning
David Baidoo-Anu and Leticia Owusu Ansah. 2023 · 2023
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Examination of Ethical Principles for LLM-Based Recommendations in Conversational AI. In 2023 International Conference on Platform Technology and Service (PlatCon) . IEEE, 109–113
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Large language model assisted software engineering: prospects, challenges, and a case study. In International Conference on Bridging the Gap between AI and Reality . Springer, 355–374
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Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios
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Uncovering chatgpt’s capabilities in recommender systems. In Proceedings of the 17th ACM Conference on Recommender Systems . 1126–1132
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Dario Di Palma. 2023 · 2023
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VideolandGPT: A User Study on a Conversational Recommender System
Mateo Gutierrez Granada, Dina Zilbershtein, Daan Odijk, and Francesco Barile. 2023 · 2023
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The user experience of ChatGPT: Findings from a questionnaire study of early users. In Proceedings of the 5th International Conference on Conversational User Interfaces . 1–10
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Llama 2: Open foundation and fine-tuned chat models
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Large language model can interpret latent space of sequential recommender
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Where to go next for recommender systems? id-vs. modality-based recommender models revisited. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2639–2649
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
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Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–21
JD Zamfirescu-Pereira, Richmond Y Wong, Bjoern Hartmann, and Qian Yang. 2023 · 2023
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Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems . 993–999
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
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Prompt Learning for News Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23) . ACM
Zizhuo Zhang and Bang Wang. 2023 · 2023
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Enhancing Recommendation Diversity by Re-ranking with Large Language Models
Diego Carraro and Derek Bridge. 2024 · 2024
Closest in time.
GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models
Emilio Ferrara. 2024 · 2024
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Prompt-Based Generative News Recommendation (PGNR): Accuracy and Controllability. In European Conference on Information Retrieval . Springer, 66–79
Xinyi Li, Yongfeng Zhang, and Edward C Malthouse. 2024 · 2024
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Evaluating Trust in Recommender Systems: A User Study on the Impacts of Explanations, Agency Attribution, and Product Types
Weizi Liu and Yanyun Wang. 2024 · 2024
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Presentations by the Humans and For the Humans: Harnessing LLMs for Generating Persona-Aware Slides from Documents. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) . 2664–2684
Ishani Mondal, S Shwetha, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, and Jordan Boyd-Graber. 2024 · 2024
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Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems. In Proceedings of the 29th International Conference on Intelligent User Interfaces . 597–608
Ítallo Silva, Leandro Marinho, Alan Said, and Martijn C Willemsen. 2024 · 2024
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Understanding User Experience in Large Language Model Interactions
Jiayin Wang, Weizhi Ma, Peijie Sun, Min Zhang, and Jian-Yun Nie. 2024 · 2024
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Lanling Xu, Junjie Zhang, Bingqian Li, Jinpeng Wang, Mingchen Cai, Wayne Xin Zhao, and Ji-Rong Wen. 2024 · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
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