Artificial Intelligence Agents Can Use Intuitive Directives For Better Simulation

Artificial intelligence agents built on large language models (LLM) are increasingly used to simulate realistic user behaviors on social media platforms. But it is still a complex problem to assess how well these agents perform. The new research shows that using intuitive guidelines (intuitive prompting) can make these agents more consistent and believer.

Basic findings of research:

• Profile loyalty: Agents can succeed in producing reactions while loyal to the given user profile
• Sezgisel guidelines: Simple, natural language guidelines perform complex instructions results better -**: Agents can also give realistic reactions to social media content that they are not trained before
• Comprehension verification: In addition to comparison with traditional human behavior, profile compatibility has also been evaluated Tags

In the study, Serbian participants were profiled through in-depth interviews and surveys, tested whether these data can be used in the training of LLM agents. The results prove that the intuitive orientation requiring low cognitive load makes agents more contextual and consistent.

This findings have the app to test social media policies and public opinion research. However, there is also a risk to manipulate public manipule, which is perceived prior to the selections of similarly believed counterfeit profiles.