8 citations · 30 across the 13 of their papers we have counts for
13 papers
Large language models simulate intersectional synthetic identities with a budget of one to two dimensions
Virgile Rennard, Christos Xypolopoulos
Large language models are increasingly used as synthetic survey respondents, promising cheap access to rare intersectional populations. We test standard demographic-persona methods…
CARTE: A Benchmark for Mapping Language Model Knowledge Across France
Sarah Almeida Carneiro, Christos Xypolopoulos, Xiao Fei +2
We introduce CARTE (Culturally Anchored Regional-Territorial Evaluation), a multiple-choice benchmark for evaluating the ability of large language models (LLMs) to discriminate fin…
GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek
Yang Zhang, Mersin Konomi, Christos Xypolopoulos +6
Large Language Models (LLMs) are commonly trained on multilingual corpora that include Greek, yet reliable evaluation benchmarks for Greek-particularly those based on authentic, na…
Bias in the Mirror: Are LLMs opinions robust to their own adversarial attacks ?
Virgile Rennard, Christos Xypolopoulos, Michalis Vazirgiannis
Large language models (LLMs) inherit biases from their training data and alignment processes, influencing their responses in subtle ways. While many studies have examined these bia…
Graph Linearization Methods for Reasoning on Graphs with Large Language Models
Christos Xypolopoulos, Guokan Shang, Xiao Fei +6
Large language models have evolved to process multiple modalities beyond text, such as images and audio, which motivates us to explore how to effectively leverage them for graph re…
GreekBART: The First Pretrained Greek Sequence-to-Sequence Model
Iakovos Evdaimon, Hadi Abdine, Christos Xypolopoulos +3
The era of transfer learning has revolutionized the fields of Computer Vision and Natural Language Processing, bringing powerful pretrained models with exceptional performance acro…
A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank
Moonjung Eo, Suhyun Kang, Wonjong Rhee
Compression has emerged as one of the essential deep learning research topics, especially for the edge devices that have limited computation power and storage capacity. Among the m…
BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets
Yanzhu Guo, Virgile Rennard, Christos Xypolopoulos +1
We introduce BERTweetFR, the first large-scale pre-trained language model for French tweets. Our model is initialized using the general-domain French language model CamemBERT which…
Evaluation Of Word Embeddings From Large-Scale French Web Content
Hadi Abdine, Christos Xypolopoulos, Moussa Kamal Eddine +1
Distributed word representations are popularly used in many tasks in natural language processing. Adding that pretrained word vectors on huge text corpus achieved high performance…
How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings
Yanzhu Guo, Christos Xypolopoulos, Michalis Vazirgiannis
Words are malleable objects, influenced by events that are reflected in written texts. Situated in the global outbreak of COVID-19, our research aims at detecting semantic shifts i…
Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
Paul Boniol, George Panagopoulos, Christos Xypolopoulos +3
Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgment to decrease the asymmetry of informatio…
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings
Christos Xypolopoulos, Antoine J. -P. Tixier, Michalis Vazirgiannis
The number of senses of a given word, or polysemy, is a very subjective notion, which varies widely across annotators and resources. We propose a novel method to estimate polysemy,…
Word Embeddings from Large-Scale Greek Web Content
Stamatis Outsios, Konstantinos Skianis, Polykarpos Meladianos +2
Word embeddings are undoubtedly very useful components in many NLP tasks. In this paper, we present word embeddings and other linguistic resources trained on the largest to date di…