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Found 16 papers, 14 papers with code
HATE-ITA: New Baselines for Hate Speech Detection in Italian
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly.
MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities
In this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) challenge.
Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection
In this paper, we provide the first benchmark study of interpretability approaches for hate speech detection.
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation
In MT, this might lead to misgendered translations, resulting, among other harms, in the perpetuation of stereotypes and prejudices.
Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features
Existing work focuses on a few spoken language understanding (SLU) tasks, and explanations are difficult to interpret for most users.
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
Training large language models to follow instructions makes them perform better on a wide range of tasks, generally becoming more helpful.
Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization
Regularized models produce better counter narratives than state-of-the-art approaches in most cases, both in terms of automatic metrics and human evaluation, especially when hateful targets are not present in the training data.
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
In this paper, we introduce a new test suite called XSTest to identify such eXaggerated Safety behaviours in a systematic way.
ITALIC: An Italian Intent Classification Dataset
Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects.
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat.
Is It Worth the (Environmental) Cost? Limited Evidence for Temporal Adaptation via Continuous Training
Consequently, we should continuously update our models with new data to expose them to new events and facts.
ferret: a Framework for Benchmarking Explainers on Transformers
With ferret, users can visualize and compare transformers-based models output explanations using state-of-the-art XAI methods on any free-text or existing XAI corpora.
EvalRS: a Rounded Evaluation of Recommender Systems
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces.
Contrastive language and vision learning of general fashion concepts
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models.
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists
EAR also reveals overfitting terms, i. e., terms most likely to induce bias, to help identify their effect on the model, task, and predictions.
Contrastive Language-Image Pre-training for the Italian Language
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts.
