Ensuring Safe and Reliable Large Language Models at Inference Time

Safety Guarantees for Large Language Models: Alignment at Inference Time

Large language models (LLMs) have made impressive progress in recent years, but the risk of generating undesirable, biased, or even harmful content remains. Traditional alignment techniques, such as Reinforcement Learning from Human Feedback (RLHF), attempt to address this problem by retraining the model. However, these procedures are resource-intensive and prone to overfitting. A new approach now promises to significantly increase the safety of LLMs during inference time, i.e., the application of the model.

Alignment without Model Modification

The focus is on the idea of formulating the safe generation of responses as a constrained Markov Decision Process (MDP) in the latent space of the LLM. Simply put, the process of text generation is viewed as a sequence of decisions in the high-dimensional space of model representations. By introducing a safety state that monitors compliance with safety guidelines, formal safety guarantees for the generated responses can be derived. This approach makes it possible to guarantee the safety of the LLM during inference time without changing the model weights – a decisive advantage over traditional alignment methods.

InferenceGuard: Practical Implementation of the Safety Concept

InferenceGuard is a concrete implementation of this theoretical approach. The system uses the described MDP approach in the latent space to monitor and control compliance with safety guidelines during text generation. In contrast to RLHF, which retrains the entire model, InferenceGuard does not interfere with the model architecture and thus avoids the costs and risks associated with retraining.

Empirical Results and Outlook

Initial empirical tests show promising results. InferenceGuard achieves high safety while maintaining good performance in terms of task completion. In experiments with various LLMs, the system was able to effectively prevent the generation of unsafe content without significantly affecting the quality of the responses. These results suggest that InferenceGuard is a promising approach for the safe application of LLMs.

Research in the field of safe AI is progressing rapidly. The development of methods like InferenceGuard, which ensure the safety of LLMs during inference time, contributes significantly to using the potential of this technology responsibly and minimizing the risks.

The combination of theoretical guarantees and practical implementation makes this approach particularly interesting for use in real-world applications. Future research could focus on extending the safety concept to more complex scenarios and integrating it into various LLM architectures. The development of robust and efficient safety mechanisms is crucial to strengthen trust in AI systems and enable their widespread application in critical areas.

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