Safeguarding large language models: a survey.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• AI Ethics

Background:

• Large Language Models (LLMs) require robust safety mechanisms (safeguards/guardrails) for ethical deployment.
• Current LLM development necessitates addressing ethical concerns within prescribed boundaries.

Purpose of the Study:

• To systematically review the current status of LLM safeguarding mechanisms.
• To identify major challenges and propose enhancements for comprehensive ethical AI governance.
• To explore techniques for evaluating, analyzing, and reinforcing LLM guardrails.

Main Methods:

• Systematic literature review of existing LLM safeguarding techniques.
• Analysis of methods used by major LLM providers and open-source communities.
• Examination of techniques for evaluating and enhancing LLM properties (hallucinations, fairness, privacy).
• Review of adversarial attacks, defenses, and reinforcement strategies for guardrails.

Main Results:

• Overview of current LLM safeguarding landscape and evaluation techniques.
• Identification of vulnerabilities, attack vectors, and defense mechanisms.
• Discussion of limitations in current methods for addressing complex ethical issues.

Conclusions:

• Current LLM safeguards face significant challenges in addressing diverse ethical issues comprehensively.
• Future enhancements require a multi-disciplinary approach, neural-symbolic methods, and integration into the systems development lifecycle.
• Developing a comprehensive guardrail system is imperative for responsible LLM deployment.