LLM Drift
LLM Drift is the gradual change in how a language model describes a company or entity over time, often leading to outdated or inaccurate information in AI-generated answers.
Definition
- LLM Drift occurs when a model's training data becomes stale or when model updates alter the weighting of different information sources.
- It manifests as slowly changing answers to the same question over weeks or months.
Monitoring and mitigation
- Regular monitoring: query LLMs with standardized questions and track answer changes.
- Baseline comparison: maintain a verified fact set and compare against LLM outputs.
- Active correction: publish updated facts through Trusted Zones and RAG APIs to counteract drift.
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