On Almost Surely Safe Alignment of Large Language Models at Inference-Time (2502.01208v3)

Abstract: We introduce a novel inference-time alignment approach for LLMs that aims to generate safe responses almost surely, i.e., with probability approaching one. Our approach models the generation of safe responses as a constrained Markov Decision Process (MDP) within the LLM's latent space. We augment a safety state that tracks the evolution of safety constraints and dynamically penalize unsafe generations to ensure the generation of safe responses. Consequently, we demonstrate formal safety guarantees w.r.t. the given cost model upon solving the MDP in the latent space with sufficiently large penalties. Building on this foundation, we propose InferenceGuard, a practical implementation that safely aligns LLMs without modifying the model weights. Empirically, we demonstrate that InferenceGuard effectively balances safety and task performance, outperforming existing inference-time alignment methods in generating safe and aligned responses. Our findings contribute to the advancement of safer LLM deployment through alignment at inference-time, thus presenting a promising alternative to resource-intensive, overfitting-prone alignment techniques like RLHF.

• The paper presents InferenceGuard, a novel method that models inference as a constrained Markov decision process and achieves safety rates of 91.04% on Alpaca-7B and 100% on Beaver-7B-v3.
• It employs state augmentation and latent-space critics to efficiently track and enforce safety constraints throughout the generation process without retraining.
• The approach integrates a modified beam search algorithm for critic-guided generation, outperforming traditional methods like ARGS, RECONTROL, and standard beam search.

An Insightful Overview of "Almost Surely Safe Alignment of LLMs at Inference-Time"

The examined paper introduces a novel methodology aimed at achieving safer LLMs during inference, termed as "InferenceGuard." The novelty of this work lies in addressing the safety concerns associated with LLM outputs without retraining the model, leveraging an inference-time alignment approach to ensure that generated responses are safe with a probability approaching one. This is accomplished via formulating the problem as a constrained Markov Decision Process (cMDP), utilizing state augmentation for safety tracking within a Markovian framework, and employing a latent-space critic to guide the generation process.

Key Contributions

1. CMdp Framework: The paper casts the inference process of LLMs as a constrained Markov decision process. This representation focuses on minimizing the expected task cost while adhering to safety constraints, effectively framing the problem within a familiar reinforcement learning context.
2. State Augmentation: Unlike typical applications of safety constraints that lack theoretical grounding for inference-time guarantees, this work employs state augmentation to track safety metrics over the course of response generation. This ensures that safety constraints are almost surely respected by mapping the cMDP to an unconstrained one, bypassing the balancing issues posed by traditional Lagrangian methods.
3. Latent-Space Critic: InferenceGuard leverages a critic-based approach operated in the latent space of the LLM. The critic provides predictions on task cost and safety compliance, effectively guiding the generation while keeping computations efficient. By transitioning analysis into latent space, the framework reduces dimensional complexity without compromising theoretical guarantees of safety.
4. Algorithm Design: The authors systematically detail a search and evaluation mechanism using a beam search-inspired algorithm, modified to incorporate critic-guided safe alignments. This ensures adaptability and efficiency, addressing potential safety violations early in the generation sequence.
5. Empirical Validation: The results demonstrate that InferenceGuard achieves a notable 91.04% and 100% safety rate for the Alpaca-7B and Beaver-7B-v3 models, respectively. The method outperforms traditional inference-time setups such as ARGS, RECONTROL, and beam search, particularly when evaluated on a safety-aligned preference dataset.

Theoretical Implications and Future Prospects

The implications for LLM deployment are twofold: practical and theoretical. Practically, this method could be rapidly integrated into existing systems to ensure safer interactions without extensive computational overhead or retraining. The approach aligns well with resource-constrained environments or applications where modifying model weights is not feasible.

Theoretically, the paper sets the stage for deeper exploration into aligning inference-time techniques with broader control strategies within deep learning frameworks. Future directions could explore the robustness against adversarial setups or extend this framework for multi-objective optimization scenarios, allowing for more nuanced applications beyond safety.

Conclusion

Overall, "Almost Surely Safe Alignment of LLMs at Inference-Time" pioneers an efficiently scalable and theoretically grounded approach to aligning LLMs for safer interaction. By focusing on inference-time adjustments and latent-space computations, the method strikes a balance between performance and operational safety, laying a practical foundation for advancing AI reliability without compromising user interaction integrity. As AI systems become increasingly pervasive, methodologies such as InferenceGuard will be pivotal in ensuring ethical and responsible deployments.