Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts
arXiv:2606.18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries. Despite their prevalence, researchers and practitioners lack methods and empirical insights to make informed decisions about when and what types of human-like behaviors LLMs should exhibit. To fill this gap, we present a multi-dimensional analysis of the prevalence, potential effects, and controllability of these behaviors using LLM-as-
The rapid advancement and widespread deployment of LLMs necessitated a deeper understanding of their increasingly complex and 'human-like' behaviors, especially as they integrate into more diverse user interactions.
Understanding and controlling human-like behaviors in LLMs is critical for effective human-AI collaboration, preventing misuse, and designing ethically sound AI systems.
This research provides a framework for analyzing, predicting, and potentially controlling the ‘human-like’ attributes of LLMs, moving beyond anecdotal observations to a more systematic approach.
- · AI developers
- · Ethical AI researchers
- · Organizations implementing LLMs
- · AI governance bodies
- · Unregulated AI applications
- · Users unaware of LLM behavioral nuances
Improved methods for training and fine-tuning LLMs to exhibit desired human-like traits or suppress undesirable ones will emerge.
This could lead to the development of 'personality profiles' or 'behavioral guardrails' for LLMs, enhancing user trust and safety.
The ability to systematically control LLM behaviors might accelerate their deployment in sensitive applications requiring specific interaction styles, potentially blurring lines between human and AI interaction in new contexts.
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Read at arXiv cs.AI