LLMs for Multi-Modal Knowledge Extraction and Analysis in Intelligence/Safety-Critical Applications

TL;DR

This paper reviews recent advances and vulnerabilities of Large Language Models, emphasizing their potential and challenges in deploying them safely in intelligence and safety-critical applications.

Contribution

It synthesizes current research on LLM assessment and vulnerabilities, providing a structured overview to guide safe application in critical domains.

Findings

LLMs are approaching human-level performance on benchmarks.

Vulnerabilities are categorized into ten high-level groups.

Mitigation strategies are reviewed for addressing these vulnerabilities.

Abstract

Large Language Models have seen rapid progress in capability in recent years; this progress has been accelerating and their capabilities, measured by various benchmarks, are beginning to approach those of humans. There is a strong demand to use such models in a wide variety of applications but, due to unresolved vulnerabilities and limitations, great care needs to be used before applying them to intelligence and safety-critical applications. This paper reviews recent literature related to LLM assessment and vulnerabilities to synthesize the current research landscape and to help understand what advances are most critical to enable use of of these technologies in intelligence and safety-critical applications. The vulnerabilities are broken down into ten high-level categories and overlaid onto a high-level life cycle of an LLM. Some general categories of mitigations are reviewed.