Search Results for author:
Found 9 papers, 5 papers with code
From Text to Source: Results in Detecting Large Language Model-Generated Content
The research also explores Model Attribution, encompassing source model identification, model family, and model size classification, in addition to quantization and watermarking detection.
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?
This paper proposes a methodology for developing and evaluating ChatGPT detectors for French text, with a focus on investigating their robustness on out-of-domain data and against common attack schemes.
Data-Efficient French Language Modeling with CamemBERTa
In this paper, we introduce CamemBERTa, a French DeBERTa model that builds upon the DeBERTaV3 architecture and training objective.
Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
The shortcomings of NLG encoder-decoder models are primarily due to the lack of Arabic datasets suitable to train NLG models such as conversational agents.
Empathetic Response Generation
Natural Language Understanding
+3
AraGPT2: Pre-Trained Transformer for Arabic Language Generation
In this paper, we develop the first advanced Arabic language generation model, AraGPT2, trained from scratch on a large Arabic corpus of internet text and news articles.
AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding
Advances in English language representation enabled a more sample-efficient pre-training task by Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA).
Multi-Task Learning using AraBert for Offensive Language Detection
The shared task on Offensive Language Detection at the OSACT4 has aimed at achieving state of art profane language detection methods for Arabic social media.
AraBERT: Transformer-based Model for Arabic Language Understanding
Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus.
Ranked #1 on
Sentiment Analysis
on AJGT
hULMonA: The Universal Language Model in Arabic
Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.
