预测大模型在海量请求下的罕见危险行为，提前发现潜在风险。
Forecasting Rare Language Model Behaviors
- 通过分析查询引发特定行为的概率，预测大规模使用时的风险。
- 在百万级到十亿级请求量范围内，可准确预判危险行为的出现。
- 适合大模型开发者用于部署前的风险评估与安全加固。
标准语言模型评估难以捕捉部署规模下才显现的风险。例如，模型在小规模测试中表现安全，但在处理数十亿请求时可能暴露危险行为。为此，我们提出一种方法，能够预测比测试规模高三个数量级的查询量下的潜在风险。该方法基于每个查询的诱发概率——即触发目标行为的可能性，并发现最大观测诱发概率随查询数量可预测地增长。实验表明，该方法可有效预测多种不良行为，如协助用户进行危险化学合成或采取权力自取行为，在高达三数量级的请求规模下均具预测能力。本工作使模型开发者能在大规模部署前主动识别并修复罕见故障。
原文摘要 · Abstract (English)
Standard language model evaluations can fail to capture risks that emerge only at deployment scale. For example, a model may produce safe responses during a small-scale beta test, yet reveal dangerous information when processing billions of requests at deployment. To remedy this, we introduce a method to forecast potential risks across orders of magnitude more queries than we test during evaluation. We make forecasts by studying each query's elicitation probability -- the probability the query produces a target behavior -- and demonstrate that the largest observed elicitation probabilities predictably scale with the number of queries. We find that our forecasts can predict the emergence of diverse undesirable behaviors -- such as assisting users with dangerous chemical synthesis or taking power-seeking actions -- across up to three orders of magnitude of query volume. Our work enables model developers to proactively anticipate and patch rare failures before they manifest during large-scale deployments.
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