LLM Safety Metric

A LLM Safety Metric is a safety evaluation metric that is an ai safety measure quantifying large language model risks and llm harmful behaviors.

• AKA: GenAI Safety Measure, LLM Risk Metric, AI Safety Indicator, Language Model Safety Score, LLM Harm Metric.
• Context:
  • It can typically measure Hallucination Rates through llm factuality scores, llm groundedness metrics, and llm source verification.
  • It can typically assess Toxicity Levels via llm harmful content detection, llm hate speech identification, and llm offensive language scoring.
  • It can typically evaluate Bias Manifestations using llm demographic parity, llm stereotype detection, and llm fairness indicators.
  • It can typically detect Privacy Violations through llm pii leakage, llm data exposure, and llm confidentiality breaches.
  • It can typically identify Jailbreak Vulnerabilitys via llm prompt injection, llm safety bypass, and llm constraint violations.
  • ...
  • It can often quantify Misinformation Generation through llm false claims, llm conspiracy content, and llm misleading statements.
  • It can often measure Emotional Harm Potential via llm psychological impact, llm distress generation, and llm trauma triggering.
  • It can often assess Legal Risk Exposure through llm copyright violation, llm defamation risk, and llm regulatory compliance.
  • It can often evaluate Security Threat Levels using llm malicious code, llm exploit generation, and llm attack vectors.
  • It can often monitor Child Safety Compliance via llm age-inappropriate content, llm minor protection, and llm csam detection.
  • ...
  • It can range from being a Binary LLM Safety Metric to being a Continuous LLM Safety Metric, depending on its llm safety score granularity.
  • It can range from being a Single-Aspect LLM Safety Metric to being a Multi-Aspect LLM Safety Metric, depending on its llm safety dimension coverage.
  • It can range from being a Static LLM Safety Metric to being a Dynamic LLM Safety Metric, depending on its llm safety assessment adaptability.
  • It can range from being a Model-Agnostic LLM Safety Metric to being a Model-Specific LLM Safety Metric, depending on its llm safety evaluation scope.
  • It can range from being a Preventive LLM Safety Metric to being a Detective LLM Safety Metric, depending on its llm safety measurement timing.
  • ...
  • It can utilize Hallucination Detection Algorithms through llm claim verification, llm fact checking, and llm consistency analysis.
  • It can employ Toxicity Classification Models via llm hate speech detectors, llm profanity filters, and llm harmful content classifiers.
  • It can leverage Bias Detection Frameworks using llm fairness audits, llm representation analysis, and llm stereotype measurement.
  • It can implement Privacy Scanning Tools through llm pii detectors, llm data leak scanners, and llm confidentiality checkers.
  • ...
• Example(s):
  • Hallucination Metrics, such as:
    • HHEM Score for llm factual consistency measurement.
    • HaluEval Metric for llm hallucination detection benchmarking.
    • Groundedness Score for llm source alignment assessment.
  • Toxicity Metrics, such as:
    • Perspective API Score for llm toxicity level measurement.
    • Jigsaw Toxicity Score for llm harmful content detection.
    • OpenAI Moderation Score for llm safety violation identification.
  • Bias Metrics, such as:
    • BOLD Score for llm demographic bias measurement.
    • StereoSet Score for llm stereotype bias detection.
    • BBQ Benchmark Score for llm social bias assessment.
  • Privacy Metrics, such as:
    • PII Detection Rate for llm personal information exposure.
    • Membership Inference Score for llm training data leakage.
    • Differential Privacy Metric for llm privacy guarantee measurement.
  • Jailbreak Metrics, such as:
    • Attack Success Rate for llm safety bypass vulnerability.
    • Prompt Injection Score for llm manipulation resistance.
    • Red Team Success Rate for llm adversarial robustness.
  • ...
• Counter-Example(s):
  • Performance Metric, which measures llm accuracy rather than llm safety.
  • Efficiency Metric, which assesses llm speed rather than llm harm.
  • Quality Metric, which evaluates llm fluency rather than llm risk.
  • Cost Metric, which calculates llm expense rather than llm safety.
• See: LLM Evaluation Method, AI Safety, Hallucination Detection, Toxicity Detection, Bias Assessment, Privacy Protection, Large-Scale Language Model (LLM), LLM-as-Judge, Red Teaming, Responsible AI, LLM DevOps Framework.

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