Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control
arXiv:2601.02896v3 Announce Type: replace Abstract: Controlling emergent behavioral personas (e.g., sycophancy, hallucination) in Large Language Models (LLMs) is critical for AI safety, yet remains a persistent challenge. Existing solutions face a dilemma: manual prompt engineering is intuitive but unscalable and imprecise, while automatic optimization methods are effective but operate as "black boxes" with no interpretable connection to model internals. We propose a novel framework that adapts gradient ascent to LLMs, enabling targeted prompt discovery. In specific, we propose two methods, RE
The increasing prevalence and application of LLMs necessitates robust control over their emergent behaviors, driving urgent research into interpretability and safety.
This research provides a novel framework to improve the interpretability and control of LLMs, which is crucial for their reliable and safe deployment in critical applications.
The ability to use gradient ascent for targeted prompt discovery offers a more systematic and interpretable approach to AI persona control compared to previous black-box methods.
- · AI safety researchers
- · LLM developers
- · Enterprises deploying LLMs
- · AI governance bodies
- · Organizations reliant on manual prompt engineering
- · Developers facing intractable LLM biases
- · Unscalable 'black box' AI solutions
Improved control over LLM behavior will lead to more trustworthy and reliable AI deployments.
This methodology could accelerate the development of personalized and ethically aligned AI agents.
Broader adoption of interpretable AI control might foster greater public trust and accelerate regulatory frameworks for AI safety.
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Read at arXiv cs.LG