This research looks at Deep Learning Language Models (LLMs) and how they can be used to improve performance on natural language tasks such as information extraction, question-answering, and summarization. They suggest Dialog-Enabled Resolving Agents (DERA) as a framework to investigate how agents charged with dialogue resolution might enhance performance on natural language tasks. Results show that DERA performs better than base GPT-4 in the care plan creation and medical conversation summarising tasks on various measures. However, they found little to no improvement in GPT-4 and DERA performance in question-answering. The paper and Github are available to explore the research further.
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