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Found 38 papers, 17 papers with code
Recalibrating classifiers for interpretable abusive content detection
We investigate the use of machine learning classifiers for detecting online abuse in empirical research.
Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection.
Online Abuse and Human Rights: WOAH Satellite Session at RightsCon 2020
In 2020 The Workshop on Online Abuse and Harms (WOAH) held a satellite panel at RightsCons 2020, an international human rights conference.
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
This work represents a crucial step toward establishing global standards for AI risk and reliability evaluation while acknowledging the need for continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories.
Why human-AI relationships need socioaffective alignment
Humans strive to design safe AI systems that align with our goals and remain under our control.
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
Finally, we explore the automation of VLM safety assessments, finding even the best safety classifiers to be lacking.
LMUnit: Fine-grained Evaluation with Natural Language Unit Tests
As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge -- human evaluation is costly and noisy, while automated metrics provide only coarse, difficult-to-interpret signals.
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications.
Risks and Opportunities of Open-Source Generative AI
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education.
WorkBench: a Benchmark Dataset for Agents in a Realistic Workplace Setting
We evaluate five existing ReAct agents on WorkBench, finding they successfully complete as few as 3% of tasks (Llama2-70B), and just 43% for the best-performing (GPT-4).
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Human feedback is central to the alignment of Large Language Models (LLMs).
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety
Researchers and practitioners have met these concerns by creating an abundance of datasets for evaluating and improving LLM safety.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
FinanceBench: A New Benchmark for Financial Question Answering
We test 16 state of the art model configurations (including GPT-4-Turbo, Llama2 and Claude2, with vector stores and long context prompts) on a sample of 150 cases from FinanceBench, and manually review their answers (n=2, 400).
Ranked #1 on
How to refund a wrong transaction in PhonePe
on How to refund a wrong transaction in PhonePe
(using extra training data)
How to refund a wrong transaction in PhonePe
Question Answering
+2
SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models
While some of the models do not give a single unsafe response, most give unsafe responses to more than 20% of the prompts, with over 50% unsafe responses in the extreme.
The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values
Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs).
The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models
In this paper, we address the concept of "alignment" in large language models (LLMs) through the lens of post-structuralist socio-political theory, specifically examining its parallels to empty signifiers.
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.
Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback
Large language models (LLMs) are used to generate content for a wide range of tasks, and are set to reach a growing audience in coming years due to integration in product interfaces like ChatGPT or search engines like Bing.
SemEval-2023 Task 10: Explainable Detection of Online Sexism
Online sexism is a widespread and harmful phenomenon.
Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning
Annotating abusive language is expensive, logistically complex and creates a risk of psychological harm.
Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models
To help address this issue, we introduce Multilingual HateCheck (MHC), a suite of functional tests for multilingual hate speech detection models.
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks
To address this issue, we propose two contrasting paradigms for data annotation.
An influencer-based approach to understanding radical right viral tweets
We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets.
Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate
We show that both text- and visual- enrichment improves model performance, with the multimodal model (0. 771) outperforming other models' F1 scores (0. 544, 0. 737, and 0. 754).
Introducing CAD: the Contextual Abuse Dataset
Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic.
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
An Expert Annotated Dataset for the Detection of Online Misogyny
Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women.
Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning
Yet, most research in online hate detection to date has focused on hateful content.
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
We provide a new dataset of ~40, 000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation.
Detecting East Asian Prejudice on Social Media
The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic.
Directions in Abusive Language Training Data: Garbage In, Garbage Out
Data-driven analysis and detection of abusive online content covers many different tasks, phenomena, contexts, and methodologies.
Islamophobes are not all the same! A study of far right actors on Twitter
Far-right actors are often purveyors of Islamophobic hate speech online, using social media to spread divisive and prejudiced messages which can stir up intergroup tensions and conflict.
Social and Information Networks Computers and Society Physics and Society Applications
Challenges and frontiers in abusive content detection
Online abusive content detection is an inherently difficult task.
Detecting weak and strong Islamophobic hate speech on social media
Islamophobic hate speech on social media inflicts considerable harm on both targeted individuals and wider society, and also risks reputational damage for the host platforms.
