2026· Journal of Communications Software and Systems· 0 citations· 49 references
TL;DR
A novel approach which combines small language models with retrieval-augmented generation in medical question answering to provide accurate and comprehensible information, and supports efficient domain knowledge updates without the need for extensive re-training.
Abstract
—This paper proposes a novel approach which combines small language models with retrieval-augmented generation in medical question answering to provide accurate and comprehensible information. Our method extracts relevant evidence from external knowledge and converts them into vector embeddings which are used for high-fidelity semantic retrieval. The small language model subsequently synthesizes the retrieved evidence into fluent, context-aware responses. Extensive experiments conducted on the PubMed benchmark dataset, the results show competitive performance to larger language models while being far more suitable for deployment in resource-constrained environments. Moreover, the proposed method supports efficient domain knowledge updates without the need for extensive re-training. Our implementation is available in the following GitHub repository: https://github.com/LeoBaoNguyen12/RagSLM-MQA.
The proposed framework offers a practical and scalable approach to mitigating hallucinations without requiring task-specific fine-tuning, highlighting the potential of retrieval-augmented approaches for trustworthy artificial intelligence (AI)-assisted healthcare applications.
This work conducts an empirical study of how irrelevant retrieved passages affect downstream generation, and proposes a lightweight, context-size classification module that dynamically predicts how much context is required based on query-specific needs.
Maya Iratni, M. Boughanem, T. Dkaki· Annual International ACM SIG...· 0 citations
The proposed REKALM, a comprehensive integration framework for enhancing LLM-based recommenders through knowledge integration, demonstrates that augmenting LLMs with lexicalized, domain-specific knowledge is an effective system-level strategy for advancing the next generation of recommender systems.
Alessandro Petruzzelli, C. Musto, Marco De Gemmis et al.· ACM Transactions on Informat...· 0 citations
This study explores a semantic variation methodology to augment training data by generating question-answer pairs with explicit control over semantic similarity, and shows that semantically controlled augmentation improves domain-specific knowledge acquisition while preserving consistency.
Alexander Chen, Caroline Tang, Jennifer Sleeman· TEXT2KG/BiKE@ESWC· 0 citations
This work proposes HybridRAG-BN, a retrieval-augmented framework for Bangla KBQA that integrates hybrid retrieval using BM25 and BGE-M3, answer generation using the GGUF version of Gemma-4-31B-Instruct, and a LoRA-fine-tuned Gemma-4-31B-Instruct model for answer verification and refinement.
This study examines the effectiveness of enhanced multilingual embedding models in improving retrieval performance for Turkish medical text data. We consider two specific medical applications in Turkish language: the TUS examination, a standardized medical assessment featuring exam questions, and Clinical QA, which involves authentic patient-physician interactions. By implementing multi-stage fine-tuning protocols on domain-specialized models, we provide detailed performance assessment and explore cross-domain transfer capabilities of the trained models. Our findings indicate that domain-specialized models improve in-domain retrieval relative to generic models, and that systematic optimization through our multi-stage pipeline yields measurable gains in retrieval precision. For instance, domain-specific fine-tuning improves TUS retrieval performance from 0.69 to 0.79 in P@1 and from 0.77 to 0.85 in MRR, while Clinical QA fine-tuning with hard-negative sampling improves P@1 from 0.33 to 0.39 and MRR from 0.41 to 0.48 relative to the vanilla encoder. In addition, reranking improves P@1 from 0.788 to 0.823 in our evaluated setting, corresponding to a 4.5% relative improvement. Furthermore, we find that, in multilingual model training, domain-specific knowledge acquired in the healthcare context of one language effectively transfers and enhances performance across other languages.
Savaş Yıldırım, Mucahit Cevik, Ayse Basar· SN Computer Science· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
