Representation Control for Large Language Models: Survey and Research Challenges

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Large language models (LLMs) are capable of completing a variety of tasks, but remain unpredictable and intractable. Representation Control (RepControl) seeks to resolve this problem through targeted interventions that modify high-level representations of concepts such as honesty, harmfulness or power-seeking. We formalize the goals and methods of RepControl to present a cohesive picture of work in this emerging field, focusing on techniques that steer model behavior by manipulating internal activations at inference time. We compare these control methods with alternative approaches, such as prompt-engineering and fine-tuning. We outline challenges such as performance degradation, computational overhead, and limitations in steering precision. We present a clear agenda for future research to build more steerable, personalized, and reliable LLMs through advances in RepControl techniques.