arXiv:2509.24319v4 Announce Type: replace-cross Abstract: Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during training, and (2) prompted expression, elicited by explicit prompts. Given their widespread use in value alignment, it is paramount to clearly understand their underlying mechanisms, particularly whether they mostly overlap (as one might expect) or rely on distinct mechanisms. We analyze this largely understudied problem at the mechanistic level using two approaches: (1) value vectors, feature direct
The rapid deployment and increasing autonomy of large language models necessitate a deeper understanding of their ethical and behavioral underpinnings.
Understanding the origins of AI values is critical for ensuring alignment, mitigating bias, and developing trustworthy artificial intelligence systems.
This research provides a mechanistic framework to distinguish between intrinsically learned values and those explicitly prompted, refining our ability to control and predict AI behavior.
- · AI ethics researchers
- · AI developers
- · Regulatory bodies
- · Developers of unaligned AI
- · Companies relying on opaque AI systems
Improved methods for auditing and steering large language models' value systems will emerge.
More robust and predictable AI agents will accelerate their integration into sensitive applications.
Enhanced understanding of AI value formation could inform pedagogical approaches for human ethical development or lead to new debates about AI sentience.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI