Naushad UzZaman

Great to see that ACM, Association for Computing Machinery recent research article, “Welfare Effects of Personalized Rankings” (https://lnkd.in/eSgABXwJ) references our Emerj Artificial Intelligence Research article on “Use Cases of Recommendation Systems in Business – Current Applications and Methods” (https://lnkd.in/g8xwmq8)  The article outlines essential insights for non-technical managers and executives on the benefits, terminology, algorithms, and current use cases among recommendation systems at companies like Amazon, Netflix, and Best Buy, along with future trends and improvements. Extra thanks to authors Robert Donnelly, Ayush Kanodia and Ilya Morozov. #BusinessInsights #Ecommerce #ConsumerGoods #Marketing #Advertising #Entertainment

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🚨 Extended Deadline - Call for Papers:  JOHD Special Collection on Language Data Reuse  🗓️ New submission deadline: 1 March 2026 This Special Collection will highlight how existing deposited mono- and multilingual language datasets have been reused in research across the humanities. Papers that present experiences of language data reuse, with reflection on encountered issues and lessons learned are welcomed. Position papers on how data creators can maximise the future reuse potential of language datasets are also encouraged.

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New Never Give Up Method Combats Training Bias in LLM Reinforcement Learning 📌 Researchers introduced the Never Give Up (NGU) method to mitigate the Matthew Effect in LLM post-training. This adaptive sampling approach reallocates compute from easy tasks to difficult problems to improve learning efficiency. 🔗 Read more: https://lnkd.in/d-B_HciA #reinforcementlearning #llm #adaptivesampling #machinelearning

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Outlining key challenges with current LLMs, highlighting opportunities in modeling variation in both language and human annotation, and outlining a path toward more inclusive and trustworthy language technologies: “Human-centered LLMs for Inclusive Language Technology: The Need to Embrace Variation Holistically in NLP“ by Barbara Plank. Main Track Invited Contributions. Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), appeared in: M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds); ACSIS, Vol. 43, pages 43–45(2025). #LLM #NLP #inclusivity #languagetechnology Open Access: https://lnkd.in/dmTnfmbt PTI - Polskie Towarzystwo Informatyczne QED Software

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📚 Highly Cited Papers in 2025 on #LargeLanguageModels, #LLM, #KnowledgeGraph and #KnowledgeExtraction — Part 1 This first collection highlights the foundational synergy between LLMs and knowledge graphs — exploring how these two paradigms complement and enhance each other. 📄 Title A Review of Large Language Models for Automated Test Case Generation 👤 Authors: Arda Celik and Qusay H. Mahmoud 🏛️ Affiliations: Ontario Tech University, Canada 🔗 Read the full paper here: https://brnw.ch/21x52BQ 📄 Title Investigating the Performance of Retrieval-Augmented Generation and Domain-Specific Fine-Tuning for the Development of AI-Driven Knowledge-Based Systems 👤 Authors: Róbert L., Péter Pollne, Andras Hajdu and Tamás Joó 🏛️ Affiliations: University of Debrecen, Neumann Nonprofit Ltd and Semmelweis University, Hungary 🔗 Read the full paper here: https://brnw.ch/21x52BS 📄 Title Knowledge Graphs and Their Reciprocal Relationship with Large Language Models 👤 Authors: Ramandeep Singh Dehal, Mehak Sharma and Enayat Rajabi 🏛️ Affiliations: Cape Breton University, Canada 🔗 Read the full paper here: https://brnw.ch/21x52BR #LLM #KnowledgeGraphs #RAG #AutomatedTesting #KnowledgeExtraction #MAKEJournal #HighlyCitedPapers

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New technique from researchers including IDSS faculty Navid Azizan enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question. https://bit.ly/4rExCo0

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Freshly published in Quantum: Connecting extended Wigner's friend arguments and noncontextuality by Laurens Walleghem, Yìlè Yīng, Rafael Wagner, and David Schmid https://lnkd.in/eKXZt58N

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