Representation Engineering for Large-Language Models: Survey and Research Challenges

Lukasz Bartoszcze; Sarthak Munshi; Bryan Sukidi; Jennifer Yen; Zejia Yang; David Williams-King; Linh Le; Kosi Asuzu; Carsten Maple

arXiv · 2502.17601

Representation Engineering for Large-Language Models: Survey and Research Challenges

Lukasz Bartoszcze·Sarthak Munshi·Bryan Sukidi·Jennifer Yen·Zejia Yang·David Williams-King·Linh Le·Kosi Asuzu·Carsten Maple

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Abstract

Large-language models are capable of completing a variety of tasks, but remain unpredictable and intractable. Representation engineering seeks to resolve this problem through a new approach utilizing samples of contrasting inputs to detect and edit high-level representations of concepts such as honesty, harmfulness or power-seeking. We formalize the goals and methods of representation engineering to present a cohesive picture of work in this emerging discipline. We compare it with alternative approaches, such as mechanistic interpretability, prompt-engineering and fine-tuning. We outline risks such as performance decrease, compute time increases and steerability issues. We present a clear agenda for future research to build predictable, dynamic, safe and personalizable LLMs.

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Lukasz Bartoszcze, Sarthak Munshi, Bryan Sukidi, Jennifer Yen, Zejia Yang, David Williams-King, Linh Le, Kosi Asuzu, Carsten Maple. 2025-02-24. Representation Engineering for Large-Language Models: Survey and Research Challenges. https://arxiv.org/abs/2502.17601

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