7 citations · 43 across the 33 of their papers we have counts for
12 papers · 1 filter
SnakModel: Lessons Learned from Training an Open Danish Large Language Model
Mike Zhang, Max Müller-Eberstein, Elisa Bassignana +1
We present SnakModel, a Danish large language model (LLM) based on Llama2-7B, which we continuously pre-train on 13.6B Danish words, and further tune on 3.7M Danish instructions. A…
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Angelika Romanou, Negar Foroutan, Anna Sotnikova +56
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal val…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers
Mike Zhang, Euan D Lindsay, Frederik Bode Thorbensen +2
End of semester student evaluations of teaching are the dominant mechanism for providing feedback to academics on their teaching practice. For large classes, however, the volume of…
Computational Job Market Analysis with Natural Language Processing
Mike Zhang
[Abridged Abstract] Recent technological advances underscore labor market dynamics, yielding significant consequences for employment prospects and increasing job vacancy data acros…
Can Humans Identify Domains?
Maria Barrett, Max Müller-Eberstein, Elisa Bassignana +3
Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance. The concept itself is, however, loos…
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Shivalika Singh, Freddie Vargus, Daniel Dsouza +30
Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to t…
Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings
Elena Senger, Mike Zhang, Rob van der Goot +1
Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in th…
Rethinking Skill Extraction in the Job Market Domain using Large Language Models
Khanh Cao Nguyen, Mike Zhang, Syrielle Montariol +1
Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes. The task is commonly tackled by training supervised models…
JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching
Antoine Magron, Anna Dai, Mike Zhang +2
Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-con…
Entity Linking in the Job Market Domain
Mike Zhang, Rob van der Goot, Barbara Plank
In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but yet remains underexplored for the job market domain. Disambiguating skill mentions can help u…
NNOSE: Nearest Neighbor Occupational Skill Extraction
Mike Zhang, Rob van der Goot, Min-Yen Kan +1
The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job descriptio…