  Ensuring large language models (LLM) behave consistently with human goals,
values, and intentions is crucial for their safety but yet computationally
expensive. To reduce the computational cost of alignment training of LLMs,
especially for those with a huge number of parameters, and to reutilize learned
value alignment, we propose ConTrans, a novel framework that enables
weak-to-strong alignment transfer via concept transplantation. From the
perspective of representation engineering, ConTrans refines concept vectors in
value alignment from a source LLM (usually a weak yet aligned LLM). The refined
concept vectors are then reformulated to adapt to the target LLM (usually a
strong yet unaligned base LLM) via affine transformation. In the third step,
ConTrans transplants the reformulated concept vectors into the residual stream
of the target LLM. Experiments demonstrate the successful transplantation of a
wide range of aligned concepts from 7B models to 13B and 70B models across
multiple LLMs and LLM families. Remarkably, ConTrans even surpasses
instruction-tuned models in terms of truthfulness. Experiment results validate
the effectiveness of both inter-LLM-family and intra-LLM-family concept
transplantation. Our work successfully demonstrates an alternative way to
achieve weak-to-strong alignment generalization and control.
