Investigating Linguistic Steering: An Analysis of Adjectival Effects Across Large Language Model Architectures

Researchers developed a Shapley-value-based framework to quantify how adjectives steer Large Language Model outputs across architectures (GPT-4o-mini, Llama-3-70b, DeepSeek-R1, Phi-3, o3). The study reveals that steering effects are model-dependent, non-universal, and exhibit complex interaction patterns—larger models show unpredictable compositional behavior while smaller models respond more literally, challenging the viability of one-size-fits-all prompting strategies.

This research addresses a critical gap in AI alignment by replacing intuitive prompting advice with quantitative attribution methods. The findings demonstrate that linguistic steering—a foundational technique for controlling LLM behavior—lacks universal principles across different architectures, complicating deployment strategies for enterprises and developers relying on consistent model behavior.

The 'family effect' observation is particularly significant: models sharing architectural lineages exhibit correlated sensitivity patterns, while fundamentally different designs produce uncorrelated responses. This suggests that prompting expertise developed on one model family may not transfer predictably to competitors. The discovery of non-additive interaction effects in larger models introduces a compositional complexity problem: adjectives don't operate independently but create synergistic or antagonistic effects whose magnitude scales unpredictably with model size.

For the AI industry, these findings underscore a fundamental trade-off: as models scale and interpret prompts more sophisticatedly, their behavior becomes harder to predict and control. This creates downstream challenges for AI safety, constitutional AI implementations, and guardrail enforcement. Organizations must now invest in model-specific alignment research rather than deploying generic prompting strategies. The research also suggests that current approaches to instruction-following and value alignment may require architectural reconsideration—smaller, more literal models may paradoxically offer greater reliability for safety-critical applications despite their reduced capability.

Future work should examine whether these steering vulnerabilities enable adversarial exploitation and whether model-specific alignment techniques can restore predictability without sacrificing performance gains.

• →Adjective steering effects vary significantly across LLM architectures, invalidating universal prompting strategies
• →Models from the same family show correlated linguistic sensitivity while different architectures respond uncorrelated to prompts
• →Larger models exhibit unpredictable non-additive interaction effects between adjectives that smaller models lack
• →Compositional complexity increases with model scale, making behavior prediction harder despite improved reasoning capability
• →Alignment efforts require model-specific calibration rather than one-size-fits-all constitutional approaches