24 citations · 40 across the 17 of their papers we have counts for
18 papers
The Commit-Abstain Circuit: Why Language Models Hallucinate Instead of Abstaining
Vy Nguyen, Ziqi Xu, Jeffrey Chan +5
Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to answer reliably. A large body of…
Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning
Vy Nguyen, Ziqi Xu, Jeffrey Chan +5
Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desirable that models abstain rathe…
Neurosymbolic Alignment for Physiologically-Safe Clinical Language Models
Abdulhady Abas Abdullah, Erik Cambria, Milena Zivkovic
Clinical LLMs can generate recommendations that are factually plausible yet physiologically unsafe. We investigate whether safety alignment can be improved by grounding preference…
TextNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention
Avni Mittal, Avinash Anand, Ashutosh Kumar +7
Can a strictly local, iterated, weight-shared computation primitive support language modelling, and which of those three properties actually drives the model's behaviour? We define…
FORGE: Fused On-Register Gradient Elimination for Memory-Efficient LLM Training
Dikshant Kukreja, Kritarth Prasad, Avinash Anand +6
Reverse-mode differentiation computes every weight gradient, writes it to memory, and only then lets the optimizer read it back. This two-phase schedule sets the memory ceiling of…
The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
Avinash Anand, Mahisha Ramesh, Avni Mittal +8
Reasoning has become central to how Large Language Models (LLMs) are evaluated and interpreted, spanning Chain-of-Thought (CoT), mathematical problem-solving, multi-hop question an…
Psychologically-Grounded Graph Modeling for Interpretable Depression Detection
Rishitej Reddy Vyalla, Kritarth Prasad, Avinash Anand +4
Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical…
IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning
Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand +8
Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consistent step-by-step reasoning, e…
Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size
Dikshant Kukreja, Kshitij Sah, Gautam Gupta +5
Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring irrelevant tokens. We formali…
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…
Mitigating Jailbreaks with Intent-Aware LLMs
Wei Jie Yeo, Ranjan Satapathy, Erik Cambria
Despite extensive safety-tuning, large language models (LLMs) remain vulnerable to jailbreak attacks via adversarially crafted instructions, reflecting a persistent trade-off betwe…
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…
Debiasing CLIP: Interpreting and Correcting Bias in Attention Heads
Wei Jie Yeo, Rui Mao, Moloud Abdar +2
Multimodal models like CLIP have gained significant attention due to their remarkable zero-shot performance across various tasks. However, studies have revealed that CLIP can inadv…
Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models
Wei Jie Yeo, Ranjan Satapathy, Erik Cambria
Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) to justify their answers. However, the faithfulness of these explanations sho…
Self-training Large Language Models through Knowledge Detection
Wei Jie Yeo, Teddy Ferdinan, Przemyslaw Kazienko +2
Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks. This paper explores a self…
How Interpretable are Reasoning Explanations from Prompting Large Language Models?
Wei Jie Yeo, Ranjan Satapathy, Rick Siow Mong Goh +1
Prompt Engineering has garnered significant attention for enhancing the performance of large language models across a multitude of tasks. Techniques such as the Chain-of-Thought no…
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal
Wei Jie Yeo, Ranjan Satapathy, Erik Cambria
The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more…
Recent Developments in Recommender Systems: A Survey
Yang Li, Kangbo Liu, Ranjan Satapathy +2
In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the cur…