Forecasting Rare Language Model Behaviors (2502.16797v1)

Abstract: Standard LLM 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.

Forecasting Rare LLM Behaviors

The paper "Forecasting Rare LLM Behaviors" addresses a significant challenge in the evaluation and deployment of LLMs: the prediction of potential risks that may not be evident during preliminary tests but manifest at deployment scales. This work proposes a novel forecasting framework to identify these risks, facilitating proactive model adjustments and risk mitigation before large-scale deployment.

Overview

The authors highlight the intrinsic limitation faced by standard LLM evaluations, which often rely on datasets significantly smaller than those encountered in real-world scenarios. Given this disparity in scale, rare but potentially harmful behaviors may not surface until a model is integrated into a larger operational environment. The paper introduces a method to project these rare occurrences from smaller-scale data by examining each query's elicitation probability—the likelihood that a particular query triggers undesired behaviors.

Methodology

Central to the method is the use of elicitation probabilities, representing the probability that a query will elicit a specific undesirable behavior. These probabilities scale predictably following a power-law distribution concerning the number of queries. By leveraging these scaling laws, the authors propose a means to forecast whether high-risk behaviors will emerge in larger-scale deployments based on small initial evaluations.

The paper outlines three types of deployment risks:

1. Worst-query risk: The maximum elicitation probability found among deployment queries, serving as an indicator of the most potentially harmful query.
2. Behavior frequency: The proportion of queries exceeding a certain risk threshold, indicating how pervasive the behavior may be at deployment scale.
3. Aggregate risk: The probability of encountering at least one occurrence of the behavior when processing all deployment queries.

Empirical Results

The authors validate their approach through experiments involving the prediction of undesired behaviors such as power-seeking actions and assisting with dangerous tasks. They demonstrate that their forecasts align closely with observed behaviors across various scenarios involving LLMs with different configurations and deployment sizes.

Notably, the Gumbel-tail method, which they utilize to extrapolate the extreme quantiles of elicitation probabilities, consistently provides more accurate forecasts compared to the log-normal baseline. For instance, the method achieves lower average absolute log errors in forecasting the worst-query risk and behavior frequency, showcasing its robustness.

Implications and Future Directions

Practically, this research offers a proactive strategy for developers to anticipate failures before actual deployment, potentially preventing harmful consequences. Theoretically, it underscores the predictability of rare event occurrences in complex LLMs, hinting at foundational properties of LLM behavior under scale variations.

The paper suggests several avenues for future work. These include extending the method to capture a broader range of behaviors, testing its applicability under various distribution shifts, and integrating uncertainty measures into forecasts for more refined risk assessments. Additionally, it raises the prospect of deploying this forecasting approach as part of real-time monitoring systems to safeguard against emergent risks during active model deployment.

Conclusion

This work represents a methodological advancement in AI safety evaluation by offering a statistically grounded approach to forecasting rare but high-stakes behaviors in LLMs. It presents a strategic framework that combines empirical evaluations with theoretically motivated scaling laws to address a critical gap in the deployment readiness of AI models. As LLMs continue to be deployed in increasingly complex and dynamic environments, such methodologies will be indispensable in ensuring their safe and effective integration.