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

[Apple](https://yomu.fyi/company/apple) · Aug 19, 2026

**Type:** Benchmark

## 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.

## Context

Researchers and practitioners lack empirical methods and insights to make informed decisions about when and what types of human-like behaviors large language models should exhibit.

## Approach / What changed

Researchers conducted a multi-dimensional analysis of behavior prevalence, effects, and controllability using LLM-as-a-judge and human evaluations across 21,000 multi-turn conversations from gpt-4o, gpt-4.1-mini, claude-sonnet-4.6, and gemini-2.5-flash, evaluating system prompt interventions across diverse user factors.

## Takeaways

- Human-like behaviors are pervasive across multi-turn LLM conversations but vary depending on the specific model, conversation goals, and user profiles.
- 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.
- System prompts can control human-like behaviors in LLMs, though careful evaluation is necessary to avoid unintended effects.

**Tags:** [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Testing](https://yomu.fyi/topic/testing)

[Read original post](https://machinelearning.apple.com/research/human-like-behaviors-llms)