123 citations · 152 across the 13 of their papers we have counts for
13 papers
Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework
Lemei Zhang, Peng Liu, Hans Dahle Kvadsheim +5
Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that over…
NorwAI's Large Language Models: Technical Report
Jon Atle Gulla, Peng Liu, Lemei Zhang
Norwegian, spoken by approximately five million people, remains underrepresented in many of the most significant breakthroughs in Natural Language Processing (NLP). To address this…
The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
Javier de la Rosa, Vladislav Mikhailov, Lemei Zhang +16
The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessi…
PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models
Lemei Zhang, Peng Liu, Marcus Tiedemann Oekland Henriksboe +3
With the rapid advancement of Natural Language Processing in recent years, numerous studies have shown that generic summaries generated by Large Language Models (LLMs) can sometime…
Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open Challenges
Lemei Zhang, Peng Liu, Yashar Deldjoo +2
The emergence of Large Language Models (LLMs) has achieved tremendous success in the field of Natural Language Processing owing to diverse training paradigms that empower LLMs to e…
NLEBench+NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in Norwegian
Peng Liu, Lemei Zhang, Terje Farup +5
Norwegian, spoken by only 5 million population, is under-representative within the most impressive breakthroughs in NLP tasks. To the best of our knowledge, there has not yet been…
Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems
Peng Liu, Lemei Zhang, Jon Atle Gulla
The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success in the field of Natural Language Processing (NLP) by learning universal representations on large…
Recommending on graphs: a comprehensive review from a data perspective
Lemei Zhang, Peng Liu, Jon Atle Gulla
Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). M…
Evaluating and Improving Context Attention Distribution on Multi-Turn Response Generation using Self-Contained Distractions
Yujie Xing, Jon Atle Gulla
Despite the rapid progress of open-domain generation-based conversational agents, most deployed systems treat dialogue contexts as single-turns, while systems dealing with multi-tu…
Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation
Yujie Xing, Jinglun Cai, Nils Barlaug +2
Open-domain conversational systems are assumed to generate equally good responses on multiple domains. Previous work achieved good performance on the single corpus, but training an…
Neural Networks for Entity Matching: A Survey
Nils Barlaug, Jon Atle Gulla
Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches…
Evaluation of team dynamic in Norwegian projects for IT students
Salah Uddin Ahmed, Ingrid Sundbø, Jon Kvisli +3
The need for teaching realistic software development in project courses has increased in a global scale. It has always been challenges in cooperating fast-changing software technol…
Female Leadership in Software Projects: A Preliminary Result on Leadership Style and Project Context Factors
Anh Nguyen-Duc, Soudabeh Khodambashi, Jon Atle Gulla +2
Women have been shown to be effective leaders in many team-based situations. However, it is also well-recognized that women are underrepresented in engineering and technology areas…