Model vs. User: Whose Values Prevail? Inference-Time Value Alignment in Multi-Turn Conversations

Title:Model vs. User: Whose Values Prevail? Inference-Time Value Alignment in Multi-Turn Conversations

Abstract:Human values shape how we expect language models to behave. Dominant alignment approaches treat values as global objectives, although the appropriate value orientation is personal and contextual. Training-free alignment can steer models toward target values at inference time, yet has been largely evaluated in single-turn settings and with a single steering direction (e.g., toward safety). We evaluate four prompt- and activation-based steering methods in multi-turn conversations, where user and assistant models push toward the same or opposing, equally desirable values. We track alignment with a linear probe across three models and two value trade-offs. When assistant and user pull in opposite directions, the side matching the assistant's baseline bias often prevails, and effects that look successful early often fade or reverse after a few turns. Steering should therefore be evaluated over full conversations and in both directions of a value trade-off.

Computation and Language (cs.CL)

arXiv:2610.19817 [cs.CL]

(or arXiv:2610.19817v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.19817 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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