62 citations · 146 across the 49 of their papers we have counts for
12 papers · 1 filter
Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective
Ernests Lavrinovics, Russa Biswas, Johannes Bjerva +1
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others.…
How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP
Kushal Tatariya, Artur Kulmizev, Wessel Poelman +6
Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in NLP. However, in recent years, such assumptions of high quality have…
Linguistically Grounded Analysis of Language Models using Shapley Head Values
Marcell Fekete, Johannes Bjerva
Understanding how linguistic knowledge is encoded in language models is crucial for improving their generalisation capabilities. In this paper, we investigate the processing of mor…
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis
Yiyi Chen, Qiongxiu Li, Russa Biswas +1
Language Confusion is a phenomenon where Large Language Models (LLMs) generate text that is neither in the desired language, nor in a contextually appropriate language. This phenom…
Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attacks
Yiyi Chen, Russa Biswas, Heather Lent +1
Large Language Models (LLMs) are susceptible to malicious influence by cyber attackers through intrusions such as adversarial, backdoor, and embedding inversion attacks. In respons…
Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers
Mike Zhang, Euan D Lindsay, Frederik Bode Thorbensen +2
End of semester student evaluations of teaching are the dominant mechanism for providing feedback to academics on their teaching practice. For large classes, however, the volume of…
A Principled Framework for Evaluating on Typologically Diverse Languages
Esther Ploeger, Wessel Poelman, Andreas Holck Høeg-Petersen +3
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform well across languages generally. However, eval…
Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification
Kushal Tatariya, Heather Lent, Johannes Bjerva +1
Emotion classification is a challenging task in NLP due to the inherent idiosyncratic and subjective nature of linguistic expression, especially with code-mixed data. Pre-trained l…
Multilingual Gradient Word-Order Typology from Universal Dependencies
Emi Baylor, Esther Ploeger, Johannes Bjerva
While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite. Existing typological dat…
What is "Typological Diversity" in NLP?
Esther Ploeger, Wessel Poelman, Miryam de Lhoneux +1
The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for multilingual NLP. However, these improvements onl…
Patterns of Persistence and Diffusibility across the World's Languages
Yiyi Chen, Johannes Bjerva
Language similarities can be caused by genetic relatedness, areal contact, universality, or chance. Colexification, i.e. a type of similarity where a single lexical form is used to…
Text Embedding Inversion Security for Multilingual Language Models
Yiyi Chen, Heather Lent, Johannes Bjerva
Textual data is often represented as real-numbered embeddings in NLP, particularly with the popularity of large language models (LLMs) and Embeddings as a Service (EaaS). However,…