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Found 11 papers, 4 papers with code
Graph Linearization Methods for Reasoning on Graphs with Large Language Models
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 reasoning tasks.
Bias in the Mirror: Are LLMs opinions robust to their own adversarial attacks ?
Large language models (LLMs) inherit biases from their training data and alignment processes, influencing their responses in subtle ways.
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties.
GreekBART: The First Pretrained Greek Sequence-to-Sequence Model
In addition, we examine its performance on two NLG tasks from GreekSUM, a newly introduced summarization dataset for the Greek language.
NLP Research and Resources at DaSciM, Ecole Polytechnique
DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, established in 2013 and since then producing research results in the area of large scale data analysis via methods of machine and deep learning.
BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets
We make our model publicly available in the transformers library with the aim of promoting future research in analytic tasks for French tweets.
Evaluation Of Word Embeddings From Large-Scale French Web Content
Adding that pretrained word vectors on huge text corpus achieved high performance in many different NLP tasks.
How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings
We employ an alignment-based approach to compare these embeddings with a general-purpose Twitter embedding unrelated to COVID-19.
Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
Artificial Intelligence techniques are already popular and important in the legal domain.
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings
A valuable by-product of our method is the ability to sample, at no extra cost, sentences containing different senses of a given word.
Word Embeddings from Large-Scale Greek Web Content
Word embeddings are undoubtedly very useful components in many NLP tasks.
