Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models
Recent surge in the accessibility of large language models (LLMs) to the general population can lead to untrackable use of such models for medical-related recommendations. Language generation via LLMs models has two key problems: firstly, they are prone to hallucination and therefore, for any medical purpose they require scientific and factual grounding; secondly, LLMs pose tremendous challenge to computational resources due to their gigantic model size. In this work, we introduce pRAGe, a pipeline for Retrieval Augmented Generation and evaluation of medical paraphrases generation using Small Language Models (SLM). We study the effectiveness of SLMs and the impact of external knowledge base for medical paraphrase generation in French.
Code (1)
Tasks
HallucinationParaphrase GenerationRetrievalRetrieval-augmented GenerationText GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System
This study introduces "RadCouncil," a multi-agent Large Language Model (LLM) framework designed to enhance the generation of impressions in radiology reports from the finding section. RadCouncil comprises three specializ…
DiagnosticLanguage ModelingLanguage ModellingLarge Language Model+1MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Education
The learning process for medical residents presents significant challenges, demanding both the ability to interpret complex case reports and the rapid acquisition of accurate medical knowledge from reliable sources. Resi…
Large language models are good medical coders, if provided with tools
This study presents a novel two-stage Retrieve-Rank system for automated ICD-10-CM medical coding, comparing its performance against a Vanilla Large Language Model (LLM) approach. Evaluating both systems on a dataset of …
Language ModelingLanguage ModellingLarge Language ModelRetrievalGeneralistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages
Sentence-embedding models for semantic search are overwhelmingly developed and evaluated on English corpora. When applied to clinical retrieval in other languages -- particularly retrieval of ICD-10-CM / CIE-10 codes -- …
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accu…
Graph LearningKnowledge GraphsLanguage ModelingLanguage Modelling+2