24 citations · 30 across the 6 of their papers we have counts for
6 papers
Beyond I'm Sorry, I Can't: Dissecting Large Language Model Refusal
Nirmalendu Prakash, Yeo Wei Jie, Amir Abdullah +3
Refusal on harmful prompts is a key safety behaviour in instruction-tuned large language models (LLMs), yet the internal causes of this behaviour remain poorly understood. We study…
Understanding Refusal in Language Models with Sparse Autoencoders
Wei Jie Yeo, Nirmalendu Prakash, Clement Neo +3
Refusal is a key safety behavior in aligned language models, yet the internal mechanisms driving refusals remain opaque. In this work, we conduct a mechanistic study of refusal in…
Interpreting Bias in Large Language Models: A Feature-Based Approach
Nirmalendu Prakash, Lee Ka Wei Roy
Large Language Models (LLMs) such as Mistral and LLaMA have showcased remarkable performance across various natural language processing (NLP) tasks. Despite their success, these mo…
SGHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Singapore
Ri Chi Ng, Nirmalendu Prakash, Ming Shan Hee +2
To address the limitations of current hate speech detection models, we introduce \textsf{SGHateCheck}, a novel framework designed for the linguistic and cultural context of Singapo…
MATK: The Meme Analytical Tool Kit
Ming Shan Hee, Aditi Kumaresan, Nguyen Khoi Hoang +3
The rise of social media platforms has brought about a new digital culture called memes. Memes, which combine visuals and text, can strongly influence public opinions on social and…
PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models
Nirmalendu Prakash, Han Wang, Nguyen Khoi Hoang +2
The proliferation of social media has given rise to a new form of communication: memes. Memes are multimodal and often contain a combination of text and visual elements that convey…