Zobrazeno 1 - 10
of 851
pro vyhledávání: '"Eberstein, A."'
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. As best practices for creating LLMs for smaller language communities ha
Externí odkaz:
http://arxiv.org/abs/2412.12956
Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical spee
Externí odkaz:
http://arxiv.org/abs/2406.04240
Autor:
Barrett, Maria, Müller-Eberstein, Max, Bassignana, Elisa, Pauli, Amalie Brogaard, Zhang, Mike, van der Goot, Rob
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, loosely defined and, in practice, refers to any non-typological property,
Externí odkaz:
http://arxiv.org/abs/2404.01785
Representational spaces learned via language modeling are fundamental to Natural Language Processing (NLP), however there has been limited understanding regarding how and when during training various types of linguistic information emerge and interac
Externí odkaz:
http://arxiv.org/abs/2310.16484
Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into
Externí odkaz:
http://arxiv.org/abs/2310.05442
Linguistic information is encoded at varying timescales (subwords, phrases, etc.) and communicative levels, such as syntax and semantics. Contextualized embeddings have analogously been found to capture these phenomena at distinctive layers and frequ
Externí odkaz:
http://arxiv.org/abs/2210.11860
With the increase in availability of large pre-trained language models (LMs) in Natural Language Processing (NLP), it becomes critical to assess their fit for a specific target task a priori - as fine-tuning the entire space of available LMs is compu
Externí odkaz:
http://arxiv.org/abs/2210.11255
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 12 (2024)
Externí odkaz:
https://doaj.org/article/d49e071ab1dc49b58f5b2dbaef7cc05d
Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored. The field of Computer Vision has begun to tackle encoder ranking, with promising forays into Natur
Externí odkaz:
http://arxiv.org/abs/2206.04935
Autor:
Ulmer, Dennis, Bassignana, Elisa, Müller-Eberstein, Max, Varab, Daniel, Zhang, Mike, van der Goot, Rob, Hardmeier, Christian, Plank, Barbara
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, compared to more established disciplines, a lack of common experimental standards rema
Externí odkaz:
http://arxiv.org/abs/2204.06251