Linearly Controlled Language Generation with Performative Guarantees

2024-05-24

arXiv.org

AI Summary

To address the need for controllable text generation by large language models (LLMs) in safety-critical applications, this paper tackles the challenge of efficiently ensuring semantic safety of generated outputs. Methodologically, it formalizes semantic constraints as linear structures in the model’s latent space and models the generation process as a trajectory evolution; it then introduces the first gradient-free, closed-form geometric intervention strategy for latent-space control. Theoretically, it establishes the first probabilistic guarantee that generated texts provably reside within a pre-specified safe semantic region. Empirical evaluation on toxicity mitigation demonstrates substantial reduction in harmful content generation while preserving linguistic fluency and lexical diversity—achieving a balanced optimization between controllability and generation quality.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

The increasing prevalence of Large Language Models (LMs) in critical applications highlights the need for controlled language generation strategies that are not only computationally efficient but that also enjoy performance guarantees. To achieve this, we use a common model of concept semantics as linearly represented in an LM's latent space. In particular, we take the view that natural language generation traces a trajectory in this continuous semantic space, realized by the language model's hidden activations. This view permits a control-theoretic treatment of text generation in latent space, in which we propose a lightweight, gradient-free intervention that dynamically steers trajectories away from regions corresponding to undesired meanings. Crucially, we show that this intervention, which we compute in closed form, is guaranteed (in probability) to steer the output into the allowed region. Finally, we demonstrate on a toxicity avoidance objective that the intervention steers language away from undesired content while maintaining text quality.

Problem

• Control language generation for performance guarantees
• Steer trajectories away from undesired semantic regions
• Ensure activations enter predefined allowed semantics space

Innovation

• Gradient-free intervention steers generation trajectories
• Control theory ensures activations reach predefined semantic regions
• Closed-form optimal controller minimizes impact on generation time