13 citations · 19 across the 15 of their papers we have counts for
15 papers
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
StaRPO: Stability-Augmented Reinforcement Policy Optimization
Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +5
Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-ans…
Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEM
Samay U. Shetty, Tharindu Cyril Weerasooriya, Deepak Pandita +1
When humans label subjective content, they disagree, and that disagreement is not noise. It reflects genuine differences in perspective shaped by annotators' social identities and…
MultiGA: Leveraging Multi-Source Seeding in Genetic Algorithms
Isabelle Diana May-Xin Ng, Tharindu Cyril Weerasooriya, Haitao Zhu +1
In this paper, we introduce, MultiGA, an optimization framework which applies genetic algorithm principles to address complex natural language tasks and reasoning problems by sampl…
LPI-RIT at LeWiDi-2025: Improving Distributional Predictions via Metadata and Loss Reweighting with DisCo
Mandira Sawkar, Samay U. Shetty, Deepak Pandita +2
The Learning With Disagreements (LeWiDi) 2025 shared task aims to model annotator disagreement through soft label distribution prediction and perspectivist evaluation, which focuse…
ProRefine: Inference-Time Prompt Refinement with Textual Feedback
Deepak Pandita, Tharindu Cyril Weerasooriya, Ankit Parag Shah +3
Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications…
Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala
Shanilka Haturusinghe, Tharindu Cyril Weerasooriya, Marcos Zampieri +2
Accurate detection of offensive language is essential for a number of applications related to social media safety. There is a sharp contrast in performance in this task between low…
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…
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…
Harnessing Business and Media Insights with Large Language Models
Yujia Bao, Ankit Parag Shah, Neeru Narang +30
This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performan…
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
Tharindu Cyril Weerasooriya, Sarah Luger, Saloni Poddar +2
Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media conten…
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…
Improving Label Quality by Jointly Modeling Items and Annotators
Tharindu Cyril Weerasooriya, Alexander G. Ororbia, Christopher M. Homan
We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering…
Neighborhood-based Pooling for Population-level Label Distribution Learning
Tharindu Cyril Weerasooriya, Tong Liu, Christopher M. Homan
Supervised machine learning often requires human-annotated data. While annotator disagreement is typically interpreted as evidence of noise, population-level label distribution lea…
KeyXtract Twitter Model - An Essential Keywords Extraction Model for Twitter Designed using NLP Tools
Tharindu Weerasooriya, Nandula Perera, S. R. Liyanage
Since a tweet is limited to 140 characters, it is ambiguous and difficult for traditional Natural Language Processing (NLP) tools to analyse. This research presents KeyXtract which…