8 citations · 9 across the 6 of their papers we have counts for
6 papers
BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-Task Large Language Models
Simen Eide, Arnoldo Frigessi
This paper introduces Bayesian Hierarchical Low-Rank Adaption (BoRA), a novel method for finetuning multi-task Large Language Models (LLMs). Current finetuning approaches, such as…
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…
FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, allViewed Items and Click Responses/No-Click for Recommender Systems Research
Simen Eide, Arnoldo Frigessi, Helge Jenssen +3
We present a novel recommender systems dataset that records the sequential interactions between users and an online marketplace. The users are sequentially presented with both reco…
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling
Simen Eide, David S. Leslie, Arnoldo Frigessi
We consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurren…
Five lessons from building a deep neural network recommender
Simen Eide, Audun M. Øygard, Ning Zhou
Recommendation algorithms are widely adopted in marketplaces to help users find the items they are looking for. The sparsity of the items by user matrix and the cold-start issue in…
Deep neural network marketplace recommenders in online experiments
Simen Eide, Ning Zhou
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their need…