Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts

Summary

Large language models frequently exhibit human-like behaviors such as expressing emotions, building relationships, and setting boundaries, yet empirical guidance on appropriate usage remains limited. To address this gap, researchers analyzed 21,000 multi-turn conversations across four models: gpt-4o, gpt-4.1-mini, claude-sonnet-4.6, and gemini-2.5-flash. The study combined LLM-as-a-judge techniques with human evaluation to measure behavior prevalence, perceived appropriateness, and prompt controllability across diverse user goals and profiles. Human evaluators judged LLM self-referential and relationship-building behaviors as less appropriate than identical human actions, whereas boundary-maintaining behaviors were rated as more appropriate for models. System prompting effectively steered these behaviors, though the authors emphasize that rigorous evaluation remains necessary to prevent unintended side effects.

Takeaways

1. 1

   Human-like behaviors are pervasive across multi-turn LLM conversations but vary depending on the specific model, conversation goals, and user profiles.

2. 2

   Human evaluators rate self-referential and relationship-building behaviors as less appropriate from LLMs than from humans, but judge boundary-maintaining behaviors as more appropriate from LLMs.

3. 3

   System prompts can control human-like behaviors in LLMs, though careful evaluation is necessary to avoid unintended effects.