TRUSS-Med: Medical Question Answering via Transformers, Retrieval, and Unified State Space Models

Abstract

The advent of Large Language Models (LLMs) and other advances in Artificial Intelligence (AI) has revolutionized natural language processing across a wide range of domains. However, applying these to high-stakes domains, such as medical question answering, remains challenging, particularly in ensuring factual accuracy and navigating complex, multi-step reasoning. We present TRUSS-Med, a system that utilizes specialized LLM agents to collaborate and automate the Medical Question Answering task by breaking it down into simpler, more manageable sub-tasks. We integrate the generative capabilities of LLMs with the structured reasoning of State Space Models (SSMs) to retrieve information, generate answers, and confirm their accuracy. Experiments on the MedQA-USMLE and MedMCQA datasets achieve strong performance, demonstrating notable improvements of 30.63 and 17.44 percentage points in accuracy over GPT-3.5. TRUSS-Med demonstrates notable performance gains over comparable models, as evidenced by results against GPT-3.5.

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