Study Reveals Overlap and Distinct Elements in Intrinsic and Prompted Value Mechanisms of Large Language Models

Global: Study Reveals Overlap and Distinct Elements in Intrinsic and Prompted Value Mechanisms of Large Language Models

A team of researchers has released a preprint on arXiv examining how large language models (LLMs) express values intrinsically versus when guided by explicit prompts. The analysis, posted in September 2025, seeks to clarify whether the two expression modes rely on the same internal mechanisms or distinct processes—a question that has received limited attention despite its relevance to value alignment efforts.

Methodological Approach

The authors employ two complementary techniques: (1) extraction of “value vectors”—directional features in the residual stream that encode value-related information, and (2) identification of “value neurons,” specific MLP neurons that contribute to those vectors. By probing these components, the study isolates the mechanistic underpinnings of both intrinsic and prompted value expression.

Shared Mechanisms

Results indicate that intrinsic and prompted value mechanisms partially share common components. These shared elements appear crucial for generating value-aligned responses and demonstrate consistency across multiple languages. Moreover, the overlapping structures reconstruct theoretical inter-value correlations observed within the model’s internal representations.

Distinct Intrinsic Elements

Despite the overlap, the intrinsic mechanism contains unique components that promote greater lexical diversity in model outputs. This diversity manifests as a broader vocabulary and more varied phrasing, suggesting that intrinsic value expression may foster richer linguistic generation independent of external prompting.

Distinct Prompted Elements

Conversely, the prompted mechanism includes exclusive components that enhance instruction following. These elements increase the model’s steerability, enabling more precise alignment with user-specified goals. Notably, the prompted components retain influence even in tasks designed to test model limits, such as jailbreaking scenarios.

Implications for Alignment Research

The findings underscore the need for nuanced alignment strategies that account for both shared and unique aspects of value expression. By recognizing that intrinsic and prompted mechanisms contribute differently to response diversity and steerability, developers may design more effective safeguards and fine‑tuning procedures. The authors suggest that future work should explore how to balance these mechanisms to achieve reliable, value‑consistent behavior across diverse applications.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.