A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents

2026-01-15

AI Summary

This study addresses a critical oversight in current large language model (LLM) style control research: the unintended side effects of targeting one stylistic dimension on other non-targeted dimensions. By leveraging controlled synthetic dialogue generation and an LLM-as-a-Judge evaluation framework, the work systematically quantifies the cross-dimensional causal impacts of style interventions in both task-oriented and open-domain settings. It reveals, for the first time, that stylistic attributes are structurally coupled rather than orthogonal. The authors introduce and release the CASSE dataset to characterize these interactions. Experiments consistently show that enforcing styles such as “conciseness” significantly undermines traits like “professionalism.” While existing mitigation strategies partially restore suppressed characteristics, they often compromise the target style itself, thereby challenging prevailing assumptions about the efficacy of current style control mechanisms.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Style features such as friendly, helpful, or concise are widely used in prompts to steer the behavior of Large Language Model (LLM) conversational agents, yet their unintended side effects remain poorly understood. In this work, we present the first systematic study of cross-feature stylistic side effects. We conduct a comprehensive survey of 127 conversational agent papers from ACL Anthology and identify 12 frequently used style features. Using controlled, synthetic dialogues across task-oriented and open domain settings, we quantify how prompting for one style feature causally affects others via a pairwise LLM as a Judge evaluation framework. Our results reveal consistent and structured side effects, such as prompting for conciseness significantly reduces perceived expertise. They demonstrate that style features are deeply entangled rather than orthogonal. To support future research, we introduce CASSE (Conversational Agent Stylistic Side Effects), a dataset capturing these complex interactions. We further evaluate prompt based and activation steering based mitigation strategies and find that while they can partially restore suppressed traits, they often degrade the primary intended style. These findings challenge the assumption of faithful style control in LLMs and highlight the need for multi-objective and more principled approaches to safe, targeted stylistic steering in conversational agents.

Problem

• stylistic side effects
• conversational agents
• large language models
• style control
• feature entanglement

Innovation

• stylistic side effects
• conversational agents
• large language models
• style entanglement
• controlled prompting