Useful LLM Documentation

Resources on useful LLM document for inclusion in scientific workflows.

TitleType of ResourceLink to ResourceDate RecordedOpen ScienceUse of LLMResearch Discipline(s)Description of Resource
Accelerating scientific breakthroughs with an AI co-scientist Documentation, Application/Tool Accelerating February 19, 2025 Open Source Research Design, Other Any Discipline We (Google Research) introduce AI co-scientist, a multi-agent AI system built with Gemini 2.0 as a virtual scientific collaborator to help scientists generate novel hypotheses and research proposals, and to accelerate the clock speed of scientific and biomedical discoveries.
How to write effective prompts for large language models Documentation, Discussion Article, Tutorial w/o Code, Application/Tool Prompt engineering November 10, 2024 Preprint Research Design, Data Collection, Data Cleaning/Preparation, Data Generation, Describing Results, Science Communication, Other Any Discipline Effectively engaging with large language models is becoming increasingly vital as they proliferate across research landscapes. This Comment presents a practical guide for understanding their capabilities and limitations, along with strategies for crafting well-structured queries, to extract maximum utility from these artificial intelligence tools.
Techniques for supercharging academic writing with generative AI Documentation, Discussion Article, Use Case Example, Reporting Guidelines, Tutorial w/ Code, Tutorial w/o Code, Application/Tool AI-based writing November 10, 2024 Preprint Describing Results, Science Communication Any Discipline Generalist large language models can elevate the quality and efficiency of academic writing.
Towards an AI policy framework in scholarly publishing Documentation, Discussion Article, Application/Tool AI publishing policy November 10, 2024 Preprint Science Communication, Other Any Discipline The rapid adoption of artificial intelligence (AI) tools in academic research raises pressing ethical concerns. I examine major publishing policies in science and medicine, uncovering inconsistencies and limitations in guiding AI usage. To encourage responsible AI integration while upholding transparency, I propose an enabling framework with author and reviewer policy templates.
Why and how to embrace AI such as ChatGPT in your academic life Research Article, Documentation, Discussion Article, Use Case Example, Tutorial w/o Code, Application/Tool Why and how to use AI in science November 10, 2024 Preprint Research Design, Data Collection, Data Cleaning/Preparation, Data Generation, Dataset Joining, Data Analysis, Describing Results, Web Scraping, Science Communication, Other Any Discipline Generative artificial intelligence (AI), including large language models (LLMs), is poised to transform scientific research, enabling researchers to elevate their research productivity. This article presents a how-to guide for employing LLMs in academic settings, focusing on their unique strengths, constraints and implications through the lens of philosophy of science and epistemology. Using ChatGPT as a case study, I identify and elaborate on three attributes contributing to its effectiveness—intelligence, versatility and collaboration—accompanied by tips on crafting effective prompts, practical use cases and a living resource online (https://osf.io/8vpwu/). Next, I evaluate the limitations of generative AI and its implications for ethical use, equality and education. Regarding ethical and responsible use, I argue from technical and epistemic standpoints that there is no need to restrict the scope or nature of AI assistance, provided that its use is transparently disclosed. A pressing challenge, however, lies in detecting fake research, which can be mitigated by embracing open science practices, such as transparent peer review and sharing data, code and materials. Addressing equality, I contend that while generative AI may promote equality for some, it may simultaneously exacerbate disparities for others—an issue with potentially significant yet unclear ramifications as it unfolds. Lastly, I consider the implications for education, advocating for active engagement with LLMs and cultivating students' critical thinking and analytical skills. The how-to guide seeks to empower researchers with the knowledge and resources necessary to effectively harness generative AI while navigating the complex ethical dilemmas intrinsic to its application.
LlamaIndex Documentation Documentation LlamaIndex documentation September 23, 2024 Open Source, Open Code Other Other LlamaIndex is the framework for Context-Augmented LLM Applications
LlamaIndex Documentation, Tutorial w/ Code LlamaIndex Tutorials September 23, 2024 Open Source, Open Code Other Other LlamaIndex is the framework for Context-Augmented LLM Applications
OLLAMA Documentation Documentation ollama September 23, 2024 Open Source, Open Code Other Other Getting Started Quickstart Examples Importing models Linux Documentation Windows Documentation Docker Documentation Reference API Reference Modelfile Reference OpenAI Compatibility Resources Troubleshooting Guide FAQ Development guide
OpenAI Cookbook Documentation, Tutorial w/ Code Cookbook April 12, 2023 Open Source, Open Code The OpenAI Cookbook shares example code for accomplishing common tasks with the OpenAI API. To run these examples, you'll need an OpenAI account and associated API key (create a free account). Most code examples are written in Python, though the concepts can be applied in any language.
OpenAI API Documentation Documentation OpenAI API April 12, 2023 Open Source The OpenAI API can be applied to virtually any task that involves understanding or generating natural language, code, or images. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.