Large Language Models (LLMs) are increasingly being adopted in enterprise environments, where employees may intentionally or accidentally submit prompts containing information that could cause legal or economic harm if leaked. Organizations may define categories of information (policies) that are prohibited from being submitted to commercial LLMs, but these definitions can vary between companies and evolve over time. This variability makes parametric classifiers less practical, as they require retraining whenever policies change. Here, we present Q-CAT, a non-parametric policy enforcement system that classifies user queries based on given categories of information that can change dynamically. The system operates by matching embedded user queries to synthetically generated queries in the database. This design enables low-cost policy updates through simple database modification, without retraining, while maintaining low latency. A comparison with strong prompt-based LLM baselines shows that Q-CAT delivers competitive performance in static environments, while offering practical advantages in dynamic policy settings, where policies change and the Query Pool can be updated from logs. Moreover, Q-CAT is up to two orders of magnitude faster than comparable-performance baselines in our experimental setup.

Inconsistent annotations in training corpora, particularly within preference learning datasets, pose challenges in developing advanced language models. These inconsistencies often arise from variability among annotators and inherent multi-dimensional nature of the preferences. To address these issues, we introduce a self-curation method that preprocesses annotated datasets by leveraging proxy models trained directly on them. Our method enhances preference learning by automatically detecting and selecting consistent annotations. We validate the proposed approach through extensive instruction-following tasks, demonstrating performance improvements of up to 33% across various learning algorithms and proxy capabilities. This work offers a straightforward and reliable solution to address preference inconsistencies without relying on heuristics, serving as an initial step toward the development of more advanced preference learning methodologies. Code is available at https://github.com/Self-Curation/ .