arXiv AI
Spillover-Aware Multi-Value Steering for Pluralistic LLM Alignment
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Adaptive Multi-Value Control in LLMs via Causal Activation Steering
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VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models
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Steering Vector Fields for Context-Aware Inference-Time Control in Large Language Models
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Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models
The paper investigates whether large language models (LLMs) possess intrinsic value systems and how to quantify and align them. By projecting responses from 106 LLMs and 95,000 human survey profiles into a shared sociological space, the authors confirm that LLMs do have values, though these values form a concentrated, idealized core rather than mirroring human diversity. They introduce the Prior-Environment-Cognition (PEC) framework to mathematically define value expression and propose an adaptive Alignment Prescription that identifies minimal interventions—ranging from prompts to targeted parameter updates—to steer LLM values efficiently without harming general performance.