Rogue Scholar Posts

language
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Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language Models Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems Author: Vaibhav Khobragade (0009–0009–8807–5982) Introduction Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like

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An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph Author: Qingqin Fang(0009–0003–5348–4264) Introduction Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.

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Improving the performance of Large Language Models Author: Dhruv Gupta ChatGPT, which first came out in late 2022, took the world by storm. Since then, various LLM models and LLM based products such as Meta’s Llama and Google’s Gemini have emerged, demonstrating the power of LLMs.

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Improving the performance and application of Large Language Models Author: Amanda Kau https://orcid.org/0009-0004-4949-9284 Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.

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How to efficiently retrieve information for different applications Author: Wenyi Pi https://orcid.org/0009-0002-2884-2771 This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system.

Published in rOpenSci - open tools for open science
Authors Tony Fischetti, Maëlle Salmon

The package assertr maintained by Tony Fischetti, provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly.The provided functionality is similar to stopifnot() but more powerful, friendly, and easier for use in pipelines.Contributed to assertr!

rOpenSci’s second cohort of champions was onboarded!Their training started with a session on code style, which we will summarize here in this post.Knowing more about code quality is relevant to all Champion projects, be it creating a new package, submitting a package to software review, or reviewing a package.This training session consisted of a talk and discussion, whereas the next package development training sessions will be more hands-on.Why