Conformal Arbitrage: Risk-Controlled Balancing of Competing Objectives in Language Models

2025-06-01

AI Summary

Deploying language models requires balancing multiple objectives—utility, safety, cost, and accuracy—while strictly bounding harmful event rates. Method: We propose a multi-objective, API-layer collaborative deployment framework that dynamically routes queries to either a primary model (optimized for accuracy) or a guard model (optimized for safety), using data-driven, posteriorly learned thresholds. An arbitration mechanism—novelly integrating conformal prediction with objective trade-off optimization—provides distribution-free risk control guarantees under limited calibration samples, without accessing model internals (e.g., parameters or gradients), thus fully decoupling deployment from training. Contribution/Results: Empirical evaluation demonstrates significant improvements in accuracy and utility under stringent safety constraints, outperforming equal-cost random routing. The framework is lightweight, model-agnostic, and seamlessly integrates into existing production pipelines.

Technology Category

• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Modern language model deployments must often balance competing objectives, for example, helpfulness versus harmlessness, cost versus accuracy, and reward versus safety. We introduce Conformal Arbitrage, a post hoc framework that learns a data driven threshold to mediate between a Primary model optimized for a primary objective and a more conservative Guardian which could be another model or a human domain expert aligned with a guardrail objective. The threshold is calibrated with conformal risk control, yielding finite sample, distribution free guarantees that the long run frequency of undesirable events, such as factual errors or safety violations, does not exceed a user specified quota. Because Conformal Arbitrage operates wholly at the API level, without requiring access to model logits or updating model weights, it complements weight based alignment techniques and integrates seamlessly with existing cost aware cascades. Empirically, Conformal Arbitrage traces an efficient frontier, allowing users to define an acceptable performance level for one objective while maximizing utility in another. We observe that our method outperforms, in terms of accuracy, cost matched random routing between models. These properties make Conformal Arbitrage a practical, theoretically grounded tool for trustworthy and economical deployment of large language models across a broad range of potentially competing objectives.

Problem

• Balancing competing objectives in language model deployments
• Controlling risk of undesirable events with conformal guarantees
• Optimizing model performance without modifying weights

Innovation

• Conformal Arbitrage mediates Primary and Guardian models
• Uses conformal risk control for safety guarantees
• Operates at API level without weight updates