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20232025
most citedVicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive

13 citations · 17 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2025★ 1 cited

What About the Scene with the Hitler Reference? HAUNT: A Framework to Probe LLMs' Self-consistency Via Adversarial Nudge

Arka Dutta, Sujan Dutta, Rijul Magu +3

Hallucinations pose a critical challenge to the real-world deployment of large language models (LLMs) in high-stakes domains. In this paper, we present a framework for stress testi…

cs.CL2024

ARTICLE: Annotator Reliability Through In-Context Learning

Sujan Dutta, Deepak Pandita, Tharindu Cyril Weerasooriya +3

Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsica…

cs.CL2024

Gender Representation and Bias in Indian Civil Service Mock Interviews

Somonnoy Banerjee, Sujan Dutta, Soumyajit Datta +1

This paper makes three key contributions. First, via a substantial corpus of 51,278 interview questions sourced from 888 YouTube videos of mock interviews of Indian civil service c…

cs.CL2024

Rater Cohesion and Quality from a Vicarious Perspective

Deepak Pandita, Tharindu Cyril Weerasooriya, Sujan Dutta +5

Human feedback is essential for building human-centered AI systems across domains where disagreement is prevalent, such as AI safety, content moderation, or sentiment analysis. Man…

cs.CL2024

Applying RLAIF for Code Generation with API-usage in Lightweight LLMs

Sujan Dutta, Sayantan Mahinder, Raviteja Anantha +1

Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains, including mitigating harm in LLM outputs, enhancing text summarizatio…

cs.CL2023★ 1 cited

Down the Toxicity Rabbit Hole: A Novel Framework to Bias Audit Large Language Models

Arka Dutta, Adel Khorramrouz, Sujan Dutta +1

This paper makes three contributions. First, it presents a generalizable, novel framework dubbed \textit{toxicity rabbit hole} that iteratively elicits toxic content from a wide su…

cs.CY2023

Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings

Sujan Dutta, Parth Srivastava, Vaishnavi Solunke +2

Divorce is the legal dissolution of a marriage by a court. Since this is usually an unpleasant outcome of a marital union, each party may have reasons to call the decision to quit…

cs.CY2023★ 2 cited

For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles

Adel Khorramrouz, Sujan Dutta, Ashiqur R. KhudaBukhsh

In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death…

cs.CL2023★ 13 cited

Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive

Tharindu Cyril Weerasooriya, Sujan Dutta, Tharindu Ranasinghe +3

Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators d…