This research investigates how the sentiment of prompts affects the quality and characteristics of large language model responses.

• Examines prompt sentiment effects on coherence, factuality, and bias in LLM outputs
• Evaluates performance across multiple domains including medical, legal, and security
• Uses both lexicon-based and transformer-based sentiment analysis methods to categorize prompts

For medical applications, understanding prompt sentiment is crucial as it can influence diagnostic suggestions, treatment recommendations, and health information accuracy—potentially impacting clinical decision support systems and patient care outcomes.

Prompt Sentiment: The Catalyst for LLM Change