by Sohail Khan. Language models are becoming integral to daily life as trusted sources of advice. While their utility has expanded from simple tasks like text summarization to more complex interactions, the empathetic quality of their responses is crucial. This article explores methods to assess the emotional appropriateness of these models, using metrics such as BLEU, ROUGE, and Sentence Transformers. By analyzing models like LLaMA in mental health dialogues, we learn that while they suffer through traditional word-based metrics, LLaMA's performance in capturing empathy through semantic similarity is promising. In addition, we must advocate for continuous monitoring to ensure these models support their users' mental well-being effectively.
