arXiv:2602.06791v2 Announce Type: replace Abstract: Being probabilistic models, during inference large language models (LLMs) display rare events: behaviour that is far from typical but highly significant. By definition all rare events are hard to see, but the enormous scale of LLM usage means that events completely unobserved during development are likely to become prominent in deployment. Here we present an end-to-end framework for the systematic analysis of rare events in LLMs. We provide a practical implementation spanning theory, efficient generation strategies, probability estimation and
The increasing deployment of large language models (LLMs) across diverse applications necessitates a robust framework for identifying and managing their unpredictable 'rare events' which manifest at scale.
This framework offers a systematic approach to understanding and mitigating critical, unexpected behaviours in LLMs, which could have significant safety, reliability, and reputational implications in real-world deployment.
The ability to proactively analyze and address rare events, rather than reactively responding to failures in deployed systems, fundamentally changes how LLMs can be developed, tested, and trusted.
- · AI safety researchers
- · LLM developers
- · Organizations deploying AI
- · Companies with inadequate AI risk management
- · Black box AI approaches
Improved reliability and safety of large language models.
Accelerated adoption of LLMs in critical applications due to enhanced trust and predictability.
Potential for new regulatory frameworks specifically addressing rare event analysis and mitigation in AI systems.
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Read at arXiv cs.LG