1 citations · 1 across the 9 of their papers we have counts for
9 papers
Learning Through Dialogue: Engagement and Efficacy Matter More Than Explanations
Shaz Furniturewala, Gerard Christopher Yeo, Kokil Jaidka
Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users' learning and engagement are understudie…
Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content
Shaz Furniturewala, Arkaitz Zubiaga
Adversarial perturbations can reduce state-of-the-art toxicity classifiers to near-zero accuracy, yet existing defences treat models as black boxes. We apply mechanistic interpreta…
Impact of Decoding Methods on Human Alignment of Conversational LLMs
Shaz Furniturewala, Kokil Jaidka, Yashvardhan Sharma
To be included into chatbot systems, Large language models (LLMs) must be aligned with human conversational conventions. However, being trained mainly on web-scraped data gives exi…
Turn-Level Empathy Prediction Using Psychological Indicators
Shaz Furniturewala, Kokil Jaidka
For the WASSA 2024 Empathy and Personality Prediction Shared Task, we propose a novel turn-level empathy detection method that decomposes empathy into six psychological indicators:…
Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis
Gerard Christopher Yeo, Shaz Furniturewala, Kokil Jaidka
Supervised machine-learning models for predicting user behavior offer a challenging classification problem with lower average prediction performance scores than other text classifi…
Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models
Shaz Furniturewala, Surgan Jandial, Abhinav Java +4
Existing debiasing techniques are typically training-based or require access to the model's internals and output distributions, so they are inaccessible to end-users looking to ada…
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Pragyan Banerjee, Abhinav Java, Surgan Jandial +4
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks. Re…
Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks
Anshika Gupta, Shaz Furniturewala, Vijay Kumari +1
A legal document is usually long and dense requiring human effort to parse it. It also contains significant amounts of jargon which make deriving insights from it using existing mo…
BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers
Shaz Furniturewala, Vijay Kumari, Amulya Ratna Dash +2
Code-Mixed text data consists of sentences having words or phrases from more than one language. Most multi-lingual communities worldwide communicate using multiple languages, with…