10 papers
Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs
Omar Mahmoud, Aly M. Kassem, Thommen George Karimpanal +4
Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific triggers or behaviors. We show th…
The Unintended Trade-off of AI Alignment:Balancing Hallucination Mitigation and Safety in LLMs
Omar Mahmoud, Ali Khalil, Buddhika Laknath Semage +2
Hallucination in large language models (LLMs) has been widely studied in recent years, with progress in both detection and mitigation aimed at improving truthfulness. Yet, a critic…
Improving Multilingual Language Models by Aligning Representations through Steering
Omar Mahmoud, Buddhika Laknath Semage, Thommen George Karimpanal +1
This paper investigates how Large Language Models (LLMs) represent non-English tokens -- a question that remains underexplored despite recent progress. We propose a lightweight int…
ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents
Kishan R. Nagiredla, Buddhika L. Semage, Arun Kumar A. +2
Co-designing autonomous robotic agents involves simultaneously optimizing the controller and physical design of the agent. Its inherent bi-level optimization formulation necessitat…
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying
Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana +4
Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plau…
Zero-shot Sim2Real Adaptation Across Environments
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Simulation based learning often provides a cost-efficient recourse to reinforcement learning applications in robotics. However, simulators are generally incapable of accurately rep…
Uncertainty Aware System Identification with Universal Policies
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real tr…
Fast Model-based Policy Search for Universal Policy Networks
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal poli…
Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation. However, such…
Recommender Systems with Random Walks: A Survey
Laknath Semage
Recommender engines have become an integral component in today's e-commerce systems. From recommending books in Amazon to finding friends in social networks such as Facebook, they…