Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning

• URL: http://arxiv.org/abs/2503.01859v1
• Date: Sun, 23 Feb 2025 20:56:31 GMT
• Title: Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning
• Authors: Jeremi I. Kaczmarek, Jakub Pokrywka, Krzysztof Biedalak, Grzegorz Kurzyp, Łukasz Grzybowski,
• Abstract summary: This paper presents a pipeline employing comments generation for Poland's State Retrieval Examination (PES) based on verified resources.<n>The system integrates these generated comments and source documents with a spaced repetition learning algorithm to enhance knowledge retention.<n> Rigorous evaluation by medical annotators demonstrates improvements in key metrics such as document relevance, credibility, and logical coherence of generated content.
• Score: 0.46603287532620746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advances in Large Language Models revolutionized medical education by enabling scalable and efficient learning solutions. This paper presents a pipeline employing Retrieval-Augmented Generation (RAG) system to prepare comments generation for Poland's State Specialization Examination (PES) based on verified resources. The system integrates these generated comments and source documents with a spaced repetition learning algorithm to enhance knowledge retention while minimizing cognitive overload. By employing a refined retrieval system, query rephraser, and an advanced reranker, our modified RAG solution promotes accuracy more than efficiency. Rigorous evaluation by medical annotators demonstrates improvements in key metrics such as document relevance, credibility, and logical coherence of generated content, proven by a series of experiments presented in the paper. This study highlights the potential of RAG systems to provide scalable, high-quality, and individualized educational resources, addressing non-English speaking users.

Related papers

• Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey [29.186229489968564]
  Retrieval-Augmented Generation (RAG) has revolutionized natural language processing by integrating Large Language Models (LLMs) with external information retrieval. evaluating RAG systems presents unique challenges due to their hybrid architecture that combines retrieval and generation components.
  arXiv  Detail & Related papers  (2025-04-21T06:39:47Z)
• Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations [65.11348389219887]
  We introduce Dialectic-RAG (DRAG), a modular approach that evaluates retrieved information by comparing, contrasting, and resolving conflicting perspectives. We show the impact of our framework both as an in-context learning strategy and for constructing demonstrations to instruct smaller models.
  arXiv  Detail & Related papers  (2025-04-07T06:55:15Z)
• ReLearn: Unlearning via Learning for Large Language Models [64.2802606302194]
  We propose ReLearn, a data augmentation and fine-tuning pipeline for effective unlearning.<n>This framework introduces Knowledge Forgetting Rate (KFR) and Knowledge Retention Rate (KRR) to measure knowledge-level preservation.<n>Our experiments show that ReLearn successfully achieves targeted forgetting while preserving high-quality output.
  arXiv  Detail & Related papers  (2025-02-16T16:31:00Z)
• Enhancing Retrieval-Augmented Generation: A Study of Best Practices [16.246719783032436]
  We develop advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. Our study systematically investigates key factors, including language model size, prompt design, document chunk size, knowledge base size, retrieval stride, query expansion techniques, and Focus Mode retrieving relevant context at sentence-level. Our findings offer actionable insights for developing RAG systems, striking a balance between contextual richness and retrieval-generation efficiency.
  arXiv  Detail & Related papers  (2025-01-13T15:07:55Z)
• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval [2.749898166276854]
  This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) By systematically extracting data from the university official webpage, we generate accurate, contextually relevant responses to user queries. Our experimental results demonstrate significant improvements in the precision and relevance of generated responses.
  arXiv  Detail & Related papers  (2024-11-09T17:38:01Z)
• VERA: Validation and Enhancement for Retrieval Augmented systems [0.0]
  We propose textbfVERA (textbfValidation and textbfEnhancement for textbfRetrieval textbfAugmented systems), a system designed to evaluate and enhance the retrieved context before response generation. VERA employs an evaluator-cum-enhancer LLM that first checks if external retrieval is necessary, evaluates the relevance and redundancy of the retrieved context, and refines it to eliminate non-essential information.
  arXiv  Detail & Related papers  (2024-09-18T16:10:47Z)
• SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation [50.26966969163348]
  Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG) Existing retrieval-augmented approaches face challenges in addressing diverse queries and documents, particularly for medical knowledge queries. We propose Self-Rewarding Tree Search (SeRTS) based on Monte Carlo Tree Search (MCTS) and a self-rewarding paradigm.
  arXiv  Detail & Related papers  (2024-06-17T06:48:31Z)
• The Power of Noise: Redefining Retrieval for RAG Systems [19.387105120040157]
  Retrieval-Augmented Generation (RAG) has emerged as a method to extend beyond the pre-trained knowledge of Large Language Models. We focus on the type of passages IR systems within a RAG solution should retrieve.
  arXiv  Detail & Related papers  (2024-01-26T14:14:59Z)
• Evaluating Generative Ad Hoc Information Retrieval [58.800799175084286]
  generative retrieval systems often directly return a grounded generated text as a response to a query. Quantifying the utility of the textual responses is essential for appropriately evaluating such generative ad hoc retrieval.
  arXiv  Detail & Related papers  (2023-11-08T14:05:00Z)
• CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks [62.22920673080208]
  Single-step generative model can dramatically simplify the search process and be optimized in end-to-end manner. We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index.
  arXiv  Detail & Related papers  (2022-08-16T10:22:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.