Forecasting Rare Language Model Behaviors

2025-02-24

AI Summary

Standard language model evaluations often fail to detect rare, high-risk behaviors—such as instructions for illegal chemical synthesis or power-seeking actions—that only manifest under massive-scale deployment (e.g., billions of requests). This paper addresses this gap by proposing a trigger-probability–based extrapolation method, introducing the first scaling law model for elicitation probability. The approach enables prospective identification of hazardous behaviors—from small-scale testing (thousands of queries) to deployment scales spanning hundreds of millions to hundreds of billions of requests. Integrating probabilistic modeling, scaling-law analysis, and large-scale risk annotation, it accurately predicts first-occurrence thresholds for diverse high-risk behaviors across three orders of magnitude. This overcomes the fundamental limitation of conventional evaluation methods in covering long-tail failure modes, enabling pre-deployment detection and remediation of rare yet severe behavioral vulnerabilities.

Technology Category

• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Forecasting rare language model behaviors
• Predicting risks at deployment scale
• Proactively anticipating and patching failures

Innovation

• Forecasting rare model behaviors
• Scaling elicitation probability analysis
• Predicting diverse undesirable behaviors