Since the advent of LLM's like GPT4 everyone in various industries has been trying to harness their power. Healthcare is an industry where this is a specifically challenging problem due to the high accuracy requirements. Prompt Engineering is a common technique used to design instructions for model responses, however, its challenges lie in the fact that the generic models may not be trained to accurately execute these specific tasks. We will present our journey of developing a cost-effective medical LLM, surpassing GPT4 in medical note-writing tasks. We'll touch upon our trials with medical prompt engineering, GPT4's limitations, and training an optimized LLM for specific medical tasks. We'll showcase multiple comparisons on model sizes, training data, and pipeline designs that enabled us to outperform GPT4 with smaller models, maintaining precision, reducing biases, preventing hallucinations, and enhancing note-writing style.

Emotion recognition in conversations (ERC) is a challenging task that has recently gained popularity due to its potential applications.Until now, however, there has been no largescale multimodal multi-party emotional conversational database containing more than two speakers per dialogue.To address this gap, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines.MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends.Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual, and textual modalities.We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.The full dataset is available for use at http:// affective-meld.github.io.