12 citations · 14 across the 11 of their papers we have counts for
5 papers · 2 filters
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…
Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios
Aditya Joshi, Diptesh Kanojia, Heather Lent +2
Despite excellent results on benchmarks over a small subset of languages, large language models struggle to process text from languages situated in `lower-resource' scenarios such…
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…
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…
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,…