6 papers · 1 filter
Intent-Hiding Jailbreaks: An Information-Theoretic Framework for Compositional Attacks
Fengwei Tian, Ravi Tandon
Recent work has shown that large language models (LLMs) can be vulnerable to jailbreak attacks in which harmful intent is obscured through composition with benign tasks. A harmful…
Adaptive Multi-Value Control in LLMs via Causal Activation Steering
Payel Bhattacharjee, Ravi Tandon
Large language models (LLMs) are increasingly deployed in settings where responses must reflect multiple, potentially interacting social norms and human values. Activation steering…
GLIDE: Guided Layerwise Hybrid Attention for Efficient LLM Inference
Vimal William, Ravi Tandon, Jyotikrishna Dass
As Large Language Models scale to increasingly long contexts, the memory I/O and computational overhead of the Key-Value (KV) cache during decoding emerges as the primary throughpu…
Semantic Smoothing via Novel View Synthesis for Robust SAR Image Classification
Daniel Brignac, Fengwei Tian, Banafsheh Latibari +2
Deep neural networks are vulnerable to adversarial perturbations, limiting deployment in safety-critical applications such as synthetic aperture radar (SAR) automatic target recogn…
STAMP: Selective Task-Aware Mechanism for Text Privacy
Fengwei Tian, Payel Bhattacharjee, Heidi Hanson +3
We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy-utility trade-off. STAMP sel…
MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling
Payel Bhattacharjee, Osvaldo Simeone, Ravi Tandon
Reward modeling is central to RLHF, RLAIF, and PPO-based alignment, but its reliability is often limited by scarce and heterogeneous human preference data. In this paper, we introd…