9 papers
FieldFormer: Locality-Aware Transformers for Spatio-Temporal Modeling on Sparse Sensor Networks
Ankit Bhardwaj, Ananth Balashankar, Lakshminarayanan Subramanian
Spatio-temporal sensor data in real-world systems is often sparse, noisy, and irregular, making latent field reconstruction fundamentally underconstrained. Under extreme sparsity,…
Breaking the Illusion: Consensus-Based Generative Mitigation of Adversarial Illusions in Multi-Modal Embeddings
Fatemeh Akbarian, Anahita Baninajjar, Yingyi Zhang +2
Multi-modal foundation models align images, text, and other modalities in a shared embedding space but remain vulnerable to adversarial illusions [35], where imperceptible perturba…
BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes
Baichuan Huang, Ananth Balashankar, Amir Aminifar
Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competitive performance, particularly i…
Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
Xiangwen Wang, Ananth Balashankar, Varun Chandrasekaran
Large language models remain vulnerable to jailbreak attacks, yet we still lack a systematic understanding of how jailbreak success scales with attacker effort across methods, mode…
Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation
Minghe Shen, Ananth Balashankar, Adam Fisch +2
The ability to rigorously estimate the failure rates of large language models (LLMs) is a prerequisite for their safe deployment. Currently, however, practitioners often face a tra…
Noisy but Valid: Robust Statistical Evaluation of LLMs with Imperfect Judges
Chen Feng, Minghe Shen, Ananth Balashankar +2
Reliable certification of Large Language Models (LLMs)-verifying that failure rates are below a safety threshold-is critical yet challenging. While "LLM-as-a-Judge" offers scalabil…