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Found 22 papers, 10 papers with code
Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups
Our study shows that (1) there are significant differences across demographic groups (including intersectional groups) on how severe they assess the harm to be, and that these differences vary across different types of safety violations, (2) the diverse rater pool captures annotation patterns that are substantially different from expert raters trained on specific set of safety policies, and (3) the differences we observe in T2I safety are distinct from previously documented group level differences in text-based safety tasks.
Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalisation of Misinformation Detection Models
This paper introduces misinfo-general, a benchmark dataset for evaluating misinformation models' ability to perform out-of-distribution generalisation.
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection
This mismatch can be partially attributed to the limitations of current evaluation setups that neglect the rapid evolution of online content and the underlying social graph.
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations
However, their robustness in the face of perturbations in training data remains a largely understudied yet critical issue.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Scientific and Creative Analogies in Pretrained Language Models
This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2.
ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs).
Prescriptive and Descriptive Approaches to Machine-Learning Transparency
We further propose a preliminary approach, called Method Cards, which aims to increase the transparency and reproducibility of ML systems by providing prescriptive documentation of commonly-used ML methods and techniques.
Ruddit: Norms of Offensiveness for English Reddit Comments
On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds.
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability
Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language.
Multilingual and cross-lingual document classification: A meta-learning approach
The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods.
Cross-Lingual Document Classification
Document Classification
+2
Meta-Learning with Sparse Experience Replay for Lifelong Language Learning
Lifelong learning requires models that can continuously learn from sequential streams of data without suffering catastrophic forgetting due to shifts in data distributions.
Graph-based Modeling of Online Communities for Fake News Detection
Existing research has modeled the structure, style, content, and patterns in dissemination of online posts, as well as the demographic traits of users who interact with them.
Joint Modelling of Emotion and Abusive Language Detection
The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online.
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples.
Node Masking: Making Graph Neural Networks Generalize and Scale Better
Graph Neural Networks (GNNs) have received a lot of interest in the recent times.
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods
Abuse on the Internet represents an important societal problem of our time.
CAMsterdam at SemEval-2019 Task 6: Neural and graph-based feature extraction for the identification of offensive tweets
We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offensive language identification in Twitter data.
Abusive Language Detection with Graph Convolutional Networks
Abuse on the Internet represents a significant societal problem of our time.
Author Profiling for Hate Speech Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet.
Neural Character-based Composition Models for Abuse Detection
The current state of the art approaches to abusive language detection, based on recurrent neural networks, do not explicitly address this problem and resort to a generic OOV (out of vocabulary) embedding for unseen words.
Author Profiling for Abuse Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet.
