3 citations · 5 across the 16 of their papers we have counts for
17 papers
When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training
Felicia Körner, Max Müller-Eberstein, Anna Korhonen +1
Training Large Language Models (LLMs) with high multilingual coverage is becoming increasingly important -- especially when monolingual resources are scarce. Recent studies have fo…
Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns
Amalie Brogaard Pauli, Maria Barrett, Max Müller-Eberstein +2
Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade. Prior work has sh…
DaKultur: Evaluating the Cultural Awareness of Language Models for Danish with Native Speakers
Max Müller-Eberstein, Mike Zhang, Elisa Bassignana +2
Large Language Models (LLMs) have seen widespread societal adoption. However, while they are able to interact with users in languages beyond English, they have been shown to lack c…
PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs
Oskar van der Wal, Pietro Lesci, Max Muller-Eberstein +4
The stability of language model pre-training and its effects on downstream performance are still understudied. Prior work shows that the training process can yield significantly di…
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…
Hypernetworks for Personalizing ASR to Atypical Speech
Max Müller-Eberstein, Dianna Yee, Karren Yang +2
Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. Ho…
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…
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training
Max Müller-Eberstein, Rob van der Goot, Barbara Plank +1
Representational spaces learned via language modeling are fundamental to Natural Language Processing (NLP), however there has been limited understanding regarding how and when duri…
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Robert Litschko, Max Müller-Eberstein, Rob van der Goot +2
Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionall…
Spectral Probing
Max Müller-Eberstein, Rob van der Goot, Barbara Plank
Linguistic information is encoded at varying timescales (subwords, phrases, etc.) and communicative levels, such as syntax and semantics. Contextualized embeddings have analogously…
Evidence > Intuition: Transferability Estimation for Encoder Selection
Elisa Bassignana, Max Müller-Eberstein, Mike Zhang +1
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 tas…
Sort by Structure: Language Model Ranking as Dependency Probing
Max Müller-Eberstein, Rob van der Goot, Barbara Plank
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…
Experimental Standards for Deep Learning in Natural Language Processing Research
Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein +5
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…
Probing for Labeled Dependency Trees
Max Müller-Eberstein, Rob van der Goot, Barbara Plank
Probing has become an important tool for analyzing representations in Natural Language Processing (NLP). For graphical NLP tasks such as dependency parsing, linear probes are curre…
How Universal is Genre in Universal Dependencies?
Max Müller-Eberstein, Rob van der Goot, Barbara Plank
This work provides the first in-depth analysis of genre in Universal Dependencies (UD). In contrast to prior work on genre identification which uses small sets of well-defined labe…
Genre as Weak Supervision for Cross-lingual Dependency Parsing
Max Müller-Eberstein, Rob van der Goot, Barbara Plank
Recent work has shown that monolingual masked language models learn to represent data-driven notions of language variation which can be used for domain-targeted training data selec…
Translating Visual Art into Music
Maximilian Müller-Eberstein, Nanne van Noord
The Synesthetic Variational Autoencoder (SynVAE) introduced in this research is able to learn a consistent mapping between visual and auditive sensory modalities in the absence of…