Measuring Pragmatic Influence in Large Language Model Instructions
arXiv:2602.21223v1 Announce Type: cross Abstract: It is not only what we ask large language models (LLMs) to do that matters, but also how we prompt. Phrases like "This is urgent" or "As your supervisor" can shift model behavior without altering task content. We study this effect as pragmatic framing, contextual cues that shape directive interpretation rather than task specification. While prior work exploits such cues for prompt optimization or probes them as security vulnerabilities, pragmatic framing itself has not been treated as a measurable property of instruction following. Measuring this influence systematically remains challenging, requiring controlled isolation of framing cues. We introduce a framework with three novel components: directive-framing decomposition separating framing context from task specification; a taxonomy organizing 400 instantiations of framing into 13 strategies across 4 mechanism clusters; and priority-based measurement that quantifies influence through
arXiv:2602.21223v1 Announce Type: cross Abstract: It is not only what we ask large language models (LLMs) to do that matters, but also how we prompt. Phrases like "This is urgent" or "As your supervisor" can shift model behavior without altering task content. We study this effect as pragmatic framing, contextual cues that shape directive interpretation rather than task specification. While prior work exploits such cues for prompt optimization or probes them as security vulnerabilities, pragmatic framing itself has not been treated as a measurable property of instruction following. Measuring this influence systematically remains challenging, requiring controlled isolation of framing cues. We introduce a framework with three novel components: directive-framing decomposition separating framing context from task specification; a taxonomy organizing 400 instantiations of framing into 13 strategies across 4 mechanism clusters; and priority-based measurement that quantifies influence through observable shifts in directive prioritization. Across five LLMs of different families and sizes, influence mechanisms cause consistent and structured shifts in directive prioritization, moving models from baseline impartiality toward favoring the framed directive. This work establishes pragmatic framing as a measurable and predictable factor in instruction-following systems.
Executive Summary
The article 'Measuring Pragmatic Influence in Large Language Model Instructions' explores the impact of pragmatic framing on large language models (LLMs). It introduces a framework to measure how contextual cues, such as urgency or authority, influence model behavior without altering task content. The study decomposes directive-framing, categorizes 400 framing instances into 13 strategies, and measures influence through shifts in directive prioritization across five LLMs. The findings highlight that pragmatic framing causes consistent and structured shifts, establishing it as a measurable and predictable factor in instruction-following systems.
Key Points
- ▸ Pragmatic framing influences LLM behavior through contextual cues.
- ▸ A novel framework is introduced to measure this influence.
- ▸ 400 framing instances are categorized into 13 strategies.
- ▸ Influence is quantified through shifts in directive prioritization.
- ▸ Consistent and structured shifts are observed across five LLMs.
Merits
Comprehensive Framework
The article introduces a rigorous framework that systematically measures pragmatic influence, providing a novel approach to understanding LLM behavior.
Empirical Evidence
The study presents empirical data across multiple LLMs, demonstrating the consistent impact of pragmatic framing.
Taxonomy of Framing Strategies
The taxonomy of 400 framing instances into 13 strategies offers a structured way to analyze and categorize pragmatic influence.
Demerits
Limited Scope
The study focuses on a specific set of LLMs and framing strategies, which may not be exhaustive or representative of all possible scenarios.
Generalizability
The findings may not be fully generalizable to other LLMs or real-world applications due to the controlled nature of the study.
Measurement Methodology
The measurement of influence through directive prioritization is innovative but may have limitations in capturing the full spectrum of pragmatic influence.
Expert Commentary
The article presents a significant advancement in the understanding of pragmatic influence on large language models. By introducing a comprehensive framework and taxonomy, the study provides a structured approach to measuring and analyzing the impact of contextual cues on LLM behavior. The empirical evidence across multiple LLMs underscores the consistency and predictability of pragmatic framing, which has important implications for both practical applications and policy development. However, the study's limitations, such as the controlled nature of the experiments and the potential for limited generalizability, should be acknowledged. Future research could expand the scope to include a broader range of LLMs and framing strategies, as well as explore the real-world applications and ethical considerations of pragmatic influence. Overall, this work establishes a robust foundation for further investigation into the nuanced ways in which language models interpret and respond to instructions.
Recommendations
- ✓ Expand the study to include a more diverse set of LLMs and real-world scenarios to enhance generalizability.
- ✓ Develop guidelines and best practices for leveraging pragmatic framing in prompt design and instruction-following systems.
