Decomposed Trust: Exploring Privacy, Adversarial Robustness, Fairness, and Ethics of Low-Rank LLMs

• URL: http://arxiv.org/abs/2511.22099v1
• Date: Thu, 27 Nov 2025 04:40:56 GMT
• Title: Decomposed Trust: Exploring Privacy, Adversarial Robustness, Fairness, and Ethics of Low-Rank LLMs
• Authors: Daniel Agyei Asante, Md Mokarram Chowdhury, Yang Li,
• Abstract summary: We study how low-rank factorization affects trustworthiness across privacy, adversarial robustness, fairness, and ethical alignment.<n>Low-rank compression preserves or improves training data privacy but weakens PII protection during conversation.<n> adversarial robustness is generally preserved and often enhanced, even under deep compression.<n>Ethical reasoning degrades in zero-shot settings but partially recovers with few-shot prompting.
• Score: 2.1311014724439845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have driven major advances across domains, yet their massive size hinders deployment in resource-constrained settings. Model compression addresses this challenge, with low-rank factorization emerging as a particularly effective method for reducing size, memory, and computation while maintaining accuracy. However, while these compressed models boast of benign performance and system-level advantages, their trustworthiness implications remain poorly understood. In this paper, we present the first comprehensive study of how low-rank factorization affects LLM trustworthiness across privacy, adversarial robustness, fairness, and ethical alignment. We evaluate multiple LLMs of different sizes and variants compressed with diverse low-rank algorithms, revealing key insights: (1) low-rank compression preserves or improves training data privacy but weakens PII protection during conversation; (2) adversarial robustness is generally preserved and often enhanced, even under deep compression; (3) ethical reasoning degrades in zero-shot settings but partially recovers with few-shot prompting; (4) fairness declines under compression. Beyond compression, we investigate how model scale and fine-tuning affect trustworthiness, as both are important in low-rank methods. To guide trustworthy compression strategies, we end our paper with a gradient-based attribution analysis to identify which layers in LLMs contribute most to adversarial robustness.

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