Search Results for author:
Found 10 papers, 4 papers with code
Fine-Grained Controllable Text Generation Using Non-Residual Prompting
We propose a resource-efficient method for converting a pre-trained CLM into this architecture, and demonstrate its potential on various experiments, including the novel task of contextualized word inclusion.
Cross-lingual and Multilingual CLIP
The long-standing endeavor of relating the textual and the visual domain recently underwent a pivotal breakthrough, as OpenAI released CLIP.
Ranked #4 on
Zero-shot Image Retrieval
on XTD10
It’s Basically the Same Language Anyway: the Case for a Nordic Language Model
When is it beneficial for a research community to organize a broader collaborative effort on a topic, and when should we instead promote individual efforts?
GANDALF: a General Character Name Description Dataset for Long Fiction
This paper introduces a long-range multiple-choice Question Answering (QA) dataset, based on full-length fiction book texts.
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish
We present GTP-SW3, a 3. 5 billion parameter autoregressive language model, trained on a newly created 100 GB Swedish corpus.
GPT-SW3: An Autoregressive Language Model for the Nordic Languages
This paper details the process of developing the first native large generative language model for the Nordic languages, GPT-SW3.
The Nordic Pile: A 1.2TB Nordic Dataset for Language Modeling
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.
Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead?
We demonstrate empirically that a large English language model coupled with modern machine translation outperforms native language models in most Scandinavian languages.
The Singleton Fallacy: Why Current Critiques of Language Models Miss the Point
By contrast, we will argue that there are many different types of language use, meaning and understanding, and that (current) language models are build with the explicit purpose of acquiring and representing one type of structural understanding of language.
Deep Representational Re-tuning using Contrastive Tension
Extracting semantically useful natural language sentence representations from pre-trained deep neural networks such as Transformers remains a challenge.
