This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct, trained for 🚀evidence relevance classification or evidence filtering🚀 in medical RAG pipelines. Given a clinical query and a candidate passage, the model outputs “Yes” if the passage contains supporting evidence and “No” otherwise. This lightweight classifier is designed to help researchers: — Improve retrieval quality in medical RAG systems. — Filter irrelevant passages before generation. — Build more reliable, interpretable RAG pipelines for medical QA. For additional context, methodology, and full experimental details, please refer to our paper below. 📄 Paper: Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights — Dataset: 3,200 query–passage pairs with expert-provided Yes/No labels (dataset to be released in a future update). — Task: Given a query and a candidate passage, the model generates «Yes» if the passage contains supporting evidence and «No» otherwise. — Objective: Causal language modeling (cross-entropy next-token loss). — Prompt: See the Quick Start section for an example usage prompt. — Hyperparameter Tuning: Five-fold…
Модальности:
Генерация текста
Области применения:
Диалог / чат RAG (поиск + генерация)
Задача: Генерация текста
Автор: Yale-BIDS-Chen
Теги: llama, rag, filtering, conversational, text-generation-inference, endpoints_compatible
Лайков: 3 | Загрузок: 26
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
