7 citations · 16 across the 12 of their papers we have counts for
14 papers
SLAyiNG: A Diverse and Community-validated Dataset of Queer Slang
Leonor Veloso, Lea Hirlimann, Lucija Mihić Zidar +3
Queer vernacular is rarely studied in NLP, despite advancements in resources and evaluation for other sociolects and informal language. Because of this, NLP systems often process q…
Derivational Morphology Reveals Analogical Generalization in Large Language Models
Valentin Hofmann, Leonie Weissweiler, David Mortensen +2
What mechanisms underlie linguistic generalization in large language models (LLMs)? This question has attracted considerable attention, with most studies analyzing the extent to wh…
Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models
Paul Röttger, Valentin Hofmann, Valentina Pyatkin +4
Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns…
Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
Fangru Lin, Emanuele La Malfa, Valentin Hofmann +3
Planning is a fundamental property of human intelligence. Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time cost…
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model
Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban +10
Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills. However, there have been relative…
MorphPiece : A Linguistic Tokenizer for Large Language Models
Haris Jabbar
Tokenization is a critical part of modern NLP pipelines. However, contemporary tokenizers for Large Language Models are based on statistical analysis of text corpora, without much…
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
We propose a fully unsupervised method to detect bias in contextualized embeddings. The method leverages the assortative information latently encoded by social networks and combine…
The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative
Leonie Weissweiler, Valentin Hofmann, Abdullatif Köksal +1
Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics. Rather than rules that operate on lexical items, it pos…
CaMEL: Case Marker Extraction without Labels
Leonie Weissweiler, Valentin Hofmann, Masoud Jalili Sabet +1
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We prop…
Geographic Adaptation of Pretrained Language Models
Valentin Hofmann, Goran Glavaš, Nikola Ljubešić +2
While pretrained language models (PLMs) have been shown to possess a plethora of linguistic knowledge, the existing body of research has largely neglected extralinguistic knowledge…
Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity
Valentin Hofmann, Xiaowen Dong, Janet B. Pierrehumbert +1
The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a mi…
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words
Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words? We present the first study investigating this question, taking B…
Dynamic Contextualized Word Embeddings
Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prio…
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model
Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
Can pretrained language models (PLMs) generate derivationally complex words? We present the first study investigating this question, taking BERT as the example PLM. We examine BERT…