The Power of Roles: Investigating the Impact of the Three Message Types on Language Model Responses

This research investigates the impact of messages roles such as user messages, system messages, and assistant messages within prompts on the accuracy and the behavior of multiple Language Models. The study specifically examines the effect of incorporating a promise for a commitment to accuracy by adding these declarations to the message history using system messages, assistant messages, and user messages. The impact of all three roles of messages is compared across multiple language models. The study evaluates the impact of locating example responses using all three roles. These investigations aim to uncover whether such messages can significantly improve the reliability and accuracy of Language Model outputs of multiple popular models. Additionally, the study aims to uncover whether harmful or biased responses can be generated using the power of roles. The results show that different message roles influence the responses of some models differently. Results also show that some models can be easily manipulated using the power of roles to generate harmful or biased responses, and the roles play a key role in jailbreaking the models.