Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?

The value orientation of Large Language Models (LLMs) has been extensively studied, as it can shape user experiences across demographic groups. However, two key challenges remain: (1) the lack of systematic comparison across value probing strategies, despite the Multiple Choice Question (MCQ) setting being vulnerable to perturbations, and (2) the uncertainty over whether probed values capture in-context information or predict models' real-world actions. In this paper, we systematically compare three widely used value probing methods: token likelihood, sequence perplexity, and text generation. Our results show that all three methods exhibit large variances under non-semantic perturbations in prompts and option formats, with sequence perplexity being the most robust overall. We further introduce two tasks to assess expressiveness: demographic prompting, testing whether probed values adapt to cultural context; and value-action agreement, testing the alignment of probed values with value-based actions. We find that demographic context has little effect on the text generation method, and probed values only weakly correlate with action preferences across all methods. Our work highlights the instability and the limited expressive power of current value probing methods, calling for more reliable LLM value representations.