4 citations · 9 across the 4 of their papers we have counts for
5 papers
Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?
Evangelia Gogoulou, Shorouq Zahra, Liane Guillou +2
A frequently observed problem with LLMs is their tendency to generate output that is nonsensical, illogical, or factually incorrect, often referred to broadly as hallucination. Bui…
Continual Learning Under Language Shift
Evangelia Gogoulou, Timothée Lesort, Magnus Boman +1
The recent increase in data and model scale for language model pre-training has led to huge training costs. In scenarios where new data become available over time, updating a model…
GPT-SW3: An Autoregressive Language Model for the Nordic Languages
Ariel Ekgren, Amaru Cuba Gyllensten, Felix Stollenwerk +7
This paper details the process of developing the first native large generative language model for the Nordic languages, GPT-SW3. We cover all parts of the development process, from…
The Nordic Pile: A 1.2TB Nordic Dataset for Language Modeling
Joey Öhman, Severine Verlinden, Ariel Ekgren +5
Pre-training Large Language Models (LLMs) require massive amounts of text data, and the performance of the LLMs typically correlates with the scale and quality of the datasets. Thi…
Cross-lingual Transfer of Monolingual Models
Evangelia Gogoulou, Ariel Ekgren, Tim Isbister +1
Recent studies in zero-shot cross-lingual learning using multilingual models have falsified the previous hypothesis that shared vocabulary and joint pre-training are the keys to cr…