9 citations · 10 across the 7 of their papers we have counts for
8 papers
SPOT: An Annotated French Corpus and Benchmark for Detecting Critical Interventions in Online Conversations
Manon Berriche, Célia Nouri, Chloée Clavel +1
We introduce SPOT (Stopping Points in Online Threads), the first annotated corpus translating the sociological concept of stopping point into a reproducible NLP task. Stopping poin…
Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights
Célia Nouri, Jean-Philippe Cointet, Chloé Clavel
Detecting abusive language in social media conversations poses significant challenges, as identifying abusiveness often depends on the conversational context, characterized by the…
Benchmarking Linguistic Diversity of Large Language Models
Yanzhu Guo, Guokan Shang, Chloé Clavel
The development and evaluation of Large Language Models (LLMs) has primarily focused on their task-solving capabilities, with recent models even surpassing human performance in som…
Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation
Cyril Chhun, Fabian M. Suchanek, Chloé Clavel
Storytelling is an integral part of human experience and plays a crucial role in social interactions. Thus, Automatic Story Evaluation (ASE) and Generation (ASG) could benefit soci…
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations
Aina Garí Soler, Matthieu Labeau, Chloé Clavel
When deriving contextualized word representations from language models, a decision needs to be made on how to obtain one for out-of-vocabulary (OOV) words that are segmented into s…
Automatic Analysis of Substantiation in Scientific Peer Reviews
Yanzhu Guo, Guokan Shang, Virgile Rennard +2
With the increasing amount of problematic peer reviews in top AI conferences, the community is urgently in need of automatic quality control measures. In this paper, we restrict ou…
The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text
Yanzhu Guo, Guokan Shang, Michalis Vazirgiannis +1
This study investigates the consequences of training language models on synthetic data generated by their predecessors, an increasingly prevalent practice given the prominence of p…
MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification
Chadi Helwe, Tom Calamai, Pierre-Henri Paris +2
We introduce MAFALDA, a benchmark for fallacy classification that merges and unites previous fallacy datasets. It comes with a taxonomy that aligns, refines, and unifies existing c…