A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 69 of its 111 references that are in the map. First posted on arXiv 15 Jan 2026.
Builds on
69 of 111 references.
References · 69
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
- 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 721
- 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 652
- 2023LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Datasetcited by 461
- 2024SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decodingcited by 247
- 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 208
- 2023Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMscited by 199
- 2025Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scalecited by 178
- 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
- 2025PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memorycited by 88
- 2023WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipediacited by 67
38 more
- 2023Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMscited by 62
- 2023Prompted LLMs as Chatbot Modules for Long Open-domain Conversationcited by 61
- 2024Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometricscited by 59
- 2023Clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agentscited by 54
- 2023Faithful Persona-based Conversational Dataset Generation with Large Language Modelscited by 47
- 2024ESCoT: Towards Interpretable Emotional Support Dialogue Systemscited by 46
- 2024Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversationscited by 43
- 2023Using In-Context Learning to Improve Dialogue Safetycited by 42
- 2024Synthetic Dialogue Dataset Generation using LLM Agentscited by 39
- 2024Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarkingcited by 35
- 2025Personalized Text Generation with Contrastive Activation Steeringcited by 34
- 2023DialogBench: Evaluating LLMs as Human-like Dialogue Systemscited by 31
- 2024Constructing Domain-Specific Evaluation Sets for LLM-as-a-judgecited by 30
- 2024Book2Dial: Generating Teacher-Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbotscited by 25
- 2024Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulationcited by 24
- 2023Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curationcited by 23
- 2024Annotation alignment: Comparing LLM and human annotations of conversational safetycited by 23
- 2024STICKERCONV: Generating Multimodal Empathetic Responses from Scratchcited by 22
- 2024MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotationscited by 22
- 2024Helpful assistant or fruitful facilitator? Investigating how personas affect language model behaviorcited by 21
- 2024WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wildcited by 20
- 2024EmpathyEar: An Open-source Avatar Multimodal Empathetic Chatbotcited by 17
- 2025Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Stylescited by 16
- 2024CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialoguescited by 15
- 2024MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generationcited by 15
- 2023Leveraging Large Language Models for Automated Dialogue Analysiscited by 14
- 2023Approximating Online Human Evaluation of Social Chatbots with Promptingcited by 11
- 2023Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologuecited by 11
- 2024KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questionscited by 11
- 2023SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentationcited by 10
- 2024LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classificationcited by 10
- 2024Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human in the Loopcited by 10
- 2023Curriculum-Driven Edubot: A Framework for Developing Language Learning Chatbots Through Synthesizing Conversational Datacited by 9
- 2023Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inferencecited by 9
- 2024Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarizationcited by 9
- 2023PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learningcited by 8
- 2024Continual Dialogue State Tracking via Reason-of-Select Distillationcited by 8
- 2024Mixed-Session Conversation with Egocentric Memorycited by 8
50 most cited of 69 All 69 (JSON)
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Forecasting Side Effects of Activation Steeringcited by 2
- 2026Forecasting Side Effects of Activation Steering28 Jul 2026
Similar, not cited
Close in wording, no citation either way
- 2023On the Effectiveness of Creating Conversational Agent Personalities Through Prompting
- 2026The Governance of Human-LLM Interaction: Safety Gating, Civility Steering, and Affective Default Lock-In
- 2026Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis
- 2026Controlling Chat Style in Language Models via Single-Direction Editing
- 2025Beyond Words: Infusing Conversational Agents with Human-like Typing Behaviors
- 2026When Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debate
- 2024LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models
- 2026Beating the Style Detector: Three Hours of Agentic Research on the AI-Text Arms Race