14 citations · 41 across the 17 of their papers we have counts for
19 papers
Can Safety Emerge from Weak Supervision? A Systematic Analysis of Small Language Models
Punyajoy Saha, Sudipta Halder, Debjyoti Mondal +1
Safety alignment is critical for deploying large language models (LLMs) in real-world applications, yet most existing approaches rely on large human-annotated datasets and static r…
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation
Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva +11
Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hatefu…
CrowdCounter: A benchmark type-specific multi-target counterspeech dataset
Punyajoy Saha, Abhilash Datta, Abhik Jana +1
Counterspeech presents a viable alternative to banning or suspending users for hate speech while upholding freedom of expression. However, writing effective counterspeech is challe…
Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management
Seid Muhie Yimam, Daryna Dementieva, Tim Fischer +8
Despite regulations imposed by nations and social media platforms, such as recent EU regulations targeting digital violence, abusive content persists as a significant challenge. Ex…
On Zero-Shot Counterspeech Generation by LLMs
Punyajoy Saha, Aalok Agrawal, Abhik Jana +2
With the emergence of numerous Large Language Models (LLM), the usage of such models in various Natural Language Processing (NLP) applications is increasing extensively. Counterspe…
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Somnath Banerjee, Maulindu Sarkar, Punyajoy Saha +2
Recently, influence functions present an apparatus for achieving explainability for deep neural models by quantifying the perturbation of individual train instances that might impa…
Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi
Mithun Das, Saurabh Kumar Pandey, Shivansh Sethi +2
With the rise of online abuse, the NLP community has begun investigating the use of neural architectures to generate counterspeech that can "counter" the vicious tone of such abusi…
Exploring the Limits of Zero Shot Vision Language Models for Hate Meme Detection: The Vulnerabilities and their Interpretations
Naquee Rizwan, Paramananda Bhaskar, Mithun Das +3
There is a rapid increase in the use of multimedia content in current social media platforms. One of the highly popular forms of such multimedia content are memes. While memes have…
Probing LLMs for hate speech detection: strengths and vulnerabilities
Sarthak Roy, Ashish Harshavardhan, Animesh Mukherjee +1
Recently efforts have been made by social media platforms as well as researchers to detect hateful or toxic language using large language models. However, none of these works aim t…
HateMM: A Multi-Modal Dataset for Hate Video Classification
Mithun Das, Rohit Raj, Punyajoy Saha +3
Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world. Due to this, hate speech research has rec…
HateProof: Are Hateful Meme Detection Systems really Robust?
Piush Aggarwal, Pranit Chawla, Mithun Das +4
Exploiting social media to spread hate has tremendously increased over the years. Lately, multi-modal hateful content such as memes has drawn relatively more traction than uni-moda…
Rationale-Guided Few-Shot Classification to Detect Abusive Language
Punyajoy Saha, Divyanshu Sheth, Kushal Kedia +2
Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, mode…
Hate Speech and Offensive Language Detection in Bengali
Mithun Das, Somnath Banerjee, Punyajoy Saha +1
Social media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender,…
Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments
Millon Madhur Das, Punyajoy Saha, Mithun Das
The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classificati…
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models
Mithun Das, Punyajoy Saha, Binny Mathew +1
Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging chal…
Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach
Mithun Das, Somnath Banerjee, Punyajoy Saha
Online hatred is a growing concern on many social media platforms. To address this issue, different social media platforms have introduced moderation policies for such content. The…
Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages
Somnath Banerjee, Maulindu Sarkar, Nancy Agrawal +2
Hate speech is considered to be one of the major issues currently plaguing online social media. Repeated and repetitive exposure to hate speech has been shown to create physiologic…
You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights
Mithun Das, Punyajoy Saha, Ritam Dutt +3
Hate speech is regarded as one of the crucial issues plaguing the online social media. The current literature on hate speech detection leverages primarily the textual content to fi…
Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection
Debjoy Saha, Naman Paharia, Debajit Chakraborty +2
Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performan…