Almost Surely Safe Alignment of Large Language Models at Inference-Time

jixiaotong, Shyam Sundhar Ramesh, Matthieu Zimmer, Bogunovic, Jun Wang, Haitham Bou Ammar

Almost Surely Safe Alignment of Large Language Models at Inference-Time: 11 upvotes on Hugging Face Daily Papers, #16 of 26 papers on 2025-02-04. Day-by-day upvote history.

Even highly capable large language models (LLMs) can produce biased or unsafe responses, and alignment techniques, such as RLHF, aimed at mitigating this issue, are expensive and prone to overfitting as they retrain the LLM. This paper introduces a novel inference-time alignment approach that ensures LLMs generate safe responses almost surely, i.e., with a probability approaching one. We achieve this by framing the safe generation of inference-time responses as a constrained Markov decision process within the LLM's latent space. Crucially, we augment a safety state that tracks the evolution of safety constraints and enables us to demonstrate formal safety guarantees upon solving the MDP in the latent space. Building on this foundation, we propose InferenceGuard, a practical implementation that safely aligns LLMs without modifying the model weights. Empirically, we demonstrate InferenceGuard effectively balances safety and task performance, outperforming existing inference-time alignment methods in generating safe and aligned responses.

Paper page on Hugging Face · arXiv

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