TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems

2024/08/17 by Xinke Jiang, Jiang, Xinke, Yue Fang +20 · 4 citations
Health Professions · #Electronic Health Records Systems #FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · pdf · doi:10.48550/arxiv.2408.09199

openalex publication_date 2024/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries. However, existing approaches to RAG fall short by neglecting system state variables, which are crucial for ensuring adaptive control, retrieval halting, and system convergence. In this paper, we introduce the TC-RAG through rigorous proof, a novel framework that addresses these challenges by incorporating a Turing Complete System to manage state variables, thereby enabling more efficient and accurate knowledge retrieval. By leveraging a memory stack system with adaptive retrieval, reasoning, and planning capabilities, TC-RAG not only ensures the controlled halting of retrieval processes but also mitigates the accumulation of erroneous knowledge via Push and Pop actions. In the case study of the medical domain, our extensive experiments on real-world healthcare datasets demonstrate the superiority of TC-RAG over existing methods in accuracy by over 7.20%. Our dataset and code have been available at https://https://github.com/Artessay/SAMA.git.

Cited by

• ADEPT: Continual Pretraining via Adaptive Expansion and Dynamic Decoupled Tuning
• HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Stores
• DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search
• EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation

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