Minimally Invasive Steering of Language Models

Problem

Unregularized reward optimization can significantly alter the output distribution of language models, leading to a degradation in generation quality. This paper addresses this gap by proposing a method that allows for effective steering of language models while maintaining the integrity of their output distributions. The work is presented as a preprint and has not undergone peer review.

Method

The authors introduce Minimally Invasive Steering Vector Optimization (MISVO), which employs a mechanism that penalizes interventions based on the local Kullback-Leibler (KL) geometry of the token distribution. The core technical contributions include:

• Gradient Computation: The method computes an analytic gradient through matrix-vector products, utilizing a frozen language model head to ensure that model parameters remain unchanged during the intervention process.
• KL Gradient Decomposition: The gradient is decomposed into an analytic Fisher term and a suffix score-function term, allowing for precise control over the steering process.
• Suffix Term Order: The suffix term is second order in terms of steering magnitude, which provides a nuanced approach to intervention.
• Fisher Surrogates: The paper discusses three Fisher surrogates that align with the full KL gradient to first order, ensuring that the steering process is both effective and efficient.
• Model Parameters: The method is applicable to models with approximately 1 billion to 14 billion parameters, indicating its scalability across different model sizes.
• Intervention Optimization: The approach allows for position-specific interventions without necessitating updates to the model parameters, thereby preserving the original model's capabilities.

Results

The proposed MISVO method achieves the highest mean reward in six out of seven model-task settings when compared to the Best-of-N baseline. Additionally, the diversity and coherence scores obtained through MISVO are reported to be close to those of the Best-of-N method, although specific numerical values for these scores are not provided in the text.

Limitations

The authors do not report any limitations in their work, and no obvious limitations are identified in the available text.

Why it matters

The implications of this work are significant for downstream applications in natural language processing, particularly in scenarios where maintaining output quality is critical while still needing to guide model behavior. The MISVO method offers a novel approach to steering language models that could enhance their usability in various applications, such as dialogue systems, content generation, and interactive AI systems.