Best AI tools for< Cite Articles >

Litero is an AI-powered writing assistant designed specifically for students. It offers a range of tools to help students with their writing tasks, including an outline generator, AI autosuggest, citation tool, and built-in ChatGPT integration. Litero is easy to use and can help students save time and improve their writing skills.

editoReview is a consulting platform and marketplace that helps academic editors and marketing agents to review the AI intelligence at the interface of research articles and service plugins API by consulting with authors and developers. It allows users to start a new review using an AI chat transcript or from a template document, cite the reference paper or app to schedule a consultation meeting with the author or developer, and pay the optional consultation and publish the review transcripts with shareable links.

CitationGenerator.AI is an AI-powered citation generator that helps users create accurate citations in APA, MLA, Chicago, and Harvard formats. The tool automatically extracts information from URLs, titles, ISBNs, or DOIs to generate precise citations. It offers a clean interface, supports multiple citation styles, and allows users to manage their research efficiently with features like import/export capabilities and custom fonts. CitationGenerator.AI prioritizes user privacy by encrypting data and offers free access without any hidden costs or ads. The tool is designed to enhance research integrity and ease by providing a user-friendly experience.

BioloGPT is an AI tool designed to answer biology-related questions with insights and graphs. It provides information on various topics such as maintaining a healthy gut microbiome, foods for a healthy immune system, effects of cannabis on the brain, risks of Covid-19 vaccines, and advancements in psoriasis treatment. The tool is updated daily and cites full papers to support its answers.

Yomu AI is an AI-powered writing assistant designed to help users write better essays, papers, and academic writing. It offers features such as an intelligent Document Assistant, AI autocomplete, paper editing tools, citation tool, plagiarism checker, and more. Yomu aims to simplify academic writing, enhance productivity, and ensure originality and authenticity in the users' work.

Yomu is an AI-powered writing assistant designed to help users with academic writing tasks such as writing essays and papers. It offers features like an intelligent Document Assistant, AI autocomplete, paper editing tools, citation tool, plagiarism checker, and more. Yomu aims to simplify the academic writing process by providing AI-powered assistance to enhance writing quality and originality.

Essay AI is a free essay-checking tool designed to help users review their essays for grammatical errors, unclear phrasing, and word misusage. It offers features such as AI autocomplete, conversation engagement, source citation, paraphrasing, rewriting, and outline building. The tool aims to save users valuable time and ensure their work meets high-quality standards. Trusted by top universities, Essay AI streamlines the essay writing process and provides instant feedback to improve writing skills.

EssayFlow is a free AI essay writer that helps students and academics write high-quality essays. It offers a range of features to make essay writing easier, including a plagiarism checker, grammar checker, and auto-completion tool. EssayFlow also provides access to a large database of academic resources, making it easy to find relevant and credible sources for your essays.

MyEssayWriter.ai is an AI-powered essay writing tool that offers advanced features to help students generate high-quality essays efficiently. The tool is designed to save time, improve writing skills, and provide unique and plagiarism-free content. With a user-friendly interface and customizable essays, MyEssayWriter.ai aims to revolutionize the writing process for students worldwide.

PaperTyper is an online writing platform that offers a range of free tools for students to use in their academic writing. These tools include an AI essay writer, plagiarism checker, grammar checker, and citation generator. PaperTyper also offers a paid service where students can hire professional essay writers to write their papers for them.

Paperguide is an AI Research Platform that offers an all-in-one solution for researchers and students to discover, read, write, manage research papers with ease. It provides AI-powered Reference Manager and Writing Assistant to help users understand papers, manage references, annotate/take notes, and supercharge their writing process. With features like AI Search, Instant Summaries, Effortless Annotations, and Flawless Citations, Paperguide aims to streamline the academic and research workflow for its users.

EssayAI is an AI-powered essay writing tool that helps users generate high-quality, plagiarism-free essays. It is designed to be undetectable by AI detectors and offers a range of features to assist writers, including smart outlining, extensive scholarly database integration, instant citation system, intelligent AI chatbot, and vast AI-driven toolsets. EssayAI can be used to write essays for various academic levels and subjects, as well as research papers, theses, case studies, and analytical reviews. It is also suitable for content writing freelancers and students who need help improving their writing skills.

The website offers an AI-powered PDF reader that allows users to chat with any PDF document. Users can upload a PDF, ask questions, get answers, extract precise sections of text, summarize, annotate, highlight, classify, analyze, translate, and more. The AI tool helps in quickly identifying key details, finding answers without reading through every word, and citing sources. It is ideal for professionals in various fields like legal, finance, research, academia, healthcare, and public sector, as well as students. The tool aims to save time, increase productivity, and simplify document management and analysis.

Jenni is an AI-powered text editor that helps you write, edit, and cite with confidence. It offers a range of features to enhance your research and writing capabilities, including autocomplete, in-text citations, paraphrasing, and a reference library. Trusted by universities and businesses worldwide, Jenni has helped over 3 million academics write over 970 million words.

Afforai is a powerful AI research assistant and chatbot that serves as an AI-powered reference manager to help researchers manage, annotate, cite papers, and conduct literature reviews with AI reliably. It offers features such as managing research papers, annotating and highlighting papers, managing citations and metadata, collaborating on notes, supporting multiple document formats, and utilizing various AI models. Afforai provides advantages like easy access to research materials, AI-powered assistance in research projects, secure data handling, compatibility with different AI models, and efficient research capabilities. However, some disadvantages include the lack of a dark mode, potential eye strain due to the bright interface, and the need for improvements in pasting output into WordPress.

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **Try out our [live research preview](https://storm.genie.stanford.edu/) to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!**

mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky (yorko). The course offers a perfect balance between theory and practice, with math formulae in lectures and practical assignments including Kaggle Inclass competitions. It is currently in a self-paced mode, guiding users through 10 weeks of content covering topics from Pandas to Gradient Boosting. The course provides articles, lectures, and assignments to enhance understanding and application of machine learning concepts.

Time-LLM is a reprogramming framework that repurposes large language models (LLMs) for time series forecasting. It allows users to treat time series analysis as a 'language task' and effectively leverage pre-trained LLMs for forecasting. The framework involves reprogramming time series data into text representations and providing declarative prompts to guide the LLM reasoning process. Time-LLM supports various backbone models such as Llama-7B, GPT-2, and BERT, offering flexibility in model selection. The tool provides a general framework for repurposing language models for time series forecasting tasks.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

SciMLBenchmarks.jl holds webpages, pdfs, and notebooks showing the benchmarks for the SciML Scientific Machine Learning Software ecosystem, including: * Benchmarks of equation solver implementations * Speed and robustness comparisons of methods for parameter estimation / inverse problems * Training universal differential equations (and subsets like neural ODEs) * Training of physics-informed neural networks (PINNs) * Surrogate comparisons, including radial basis functions, neural operators (DeepONets, Fourier Neural Operators), and more The SciML Bench suite is made to be a comprehensive open source benchmark from the ground up, covering the methods of computational science and scientific computing all the way to AI for science.

This repository focuses on exploring tools that enhance the performance of language models for various tasks. It provides a structured list of literature relevant to tool-augmented language models, covering topics such as tool basics, tool use paradigm, scenarios, advanced methods, and evaluation. The repository includes papers, preprints, and books that discuss the use of tools in conjunction with language models for tasks like reasoning, question answering, mathematical calculations, accessing knowledge, interacting with the world, and handling non-textual modalities.

Slideflow is a deep learning library for digital pathology, offering a user-friendly interface for model development. It is designed for medical researchers and AI enthusiasts, providing an accessible platform for developing state-of-the-art pathology models. Slideflow offers customizable training pipelines, robust slide processing and stain normalization toolkit, support for weakly-supervised or strongly-supervised labels, built-in foundation models, multiple-instance learning, self-supervised learning, generative adversarial networks, explainability tools, layer activation analysis tools, uncertainty quantification, interactive user interface for model deployment, and more. It supports both PyTorch and Tensorflow, with optional support for Libvips for slide reading. Slideflow can be installed via pip, Docker container, or from source, and includes non-commercial add-ons for additional tools and pretrained models. It allows users to create projects, extract tiles from slides, train models, and provides evaluation tools like heatmaps and mosaic maps.

This repository maintains a list of research papers on LLM-based Multi-Agents, categorized into five main streams: Multi-Agents Framework, Multi-Agents Orchestration and Efficiency, Multi-Agents for Problem Solving, Multi-Agents for World Simulation, and Multi-Agents Datasets and Benchmarks. The repository also includes a survey paper on LLM-based Multi-Agents and a table summarizing the key findings of the survey.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron. We're releasing it with the community in the spirit of building in the open. Note that it is still very much early so don't expect 100% stability ^^' In case of problems or question, feel free to open an issue!

DB-GPT is a personal database administrator that can solve database problems by reading documents, using various tools, and writing analysis reports. It is currently undergoing an upgrade. **Features:** * **Online Demo:** * Import documents into the knowledge base * Utilize the knowledge base for well-founded Q&A and diagnosis analysis of abnormal alarms * Send feedbacks to refine the intermediate diagnosis results * Edit the diagnosis result * Browse all historical diagnosis results, used metrics, and detailed diagnosis processes * **Language Support:** * English (default) * Chinese (add "language: zh" in config.yaml) * **New Frontend:** * Knowledgebase + Chat Q&A + Diagnosis + Report Replay * **Extreme Speed Version for localized llms:** * 4-bit quantized LLM (reducing inference time by 1/3) * vllm for fast inference (qwen) * Tiny LLM * **Multi-path extraction of document knowledge:** * Vector database (ChromaDB) * RESTful Search Engine (Elasticsearch) * **Expert prompt generation using document knowledge** * **Upgrade the LLM-based diagnosis mechanism:** * Task Dispatching -> Concurrent Diagnosis -> Cross Review -> Report Generation * Synchronous Concurrency Mechanism during LLM inference * **Support monitoring and optimization tools in multiple levels:** * Monitoring metrics (Prometheus) * Flame graph in code level * Diagnosis knowledge retrieval (dbmind) * Logical query transformations (Calcite) * Index optimization algorithms (for PostgreSQL) * Physical operator hints (for PostgreSQL) * Backup and Point-in-time Recovery (Pigsty) * **Continuously updated papers and experimental reports** This project is constantly evolving with new features. Don't forget to star ⭐ and watch 👀 to stay up to date.

SeaLLMs are a family of language models optimized for Southeast Asian (SEA) languages. They were pre-trained from Llama-2, on a tailored publicly-available dataset, which comprises texts in Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. The SeaLLM-chat underwent supervised finetuning (SFT) and specialized self-preferencing DPO using a mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which **adapt to the local cultural norms, customs, styles and laws in these areas**. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform **ChatGPT-3.5** in non-Latin languages, such as Thai, Khmer, Lao, and Burmese.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

Expert in article-based reference generation and formatting.

An AI scholar providing detailed academic assistance.

Experto en APA 7ma Edición y redacción de artículos científicos

Expert in all academic in-text citation styles.

Expert in all academic in-text citation styles.

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I turn citation details into formatted tables.

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Expert in citation styles and reference generation.

Input your work and harvard referencing will be automated for you

Generates legal citations based on the Indigo Book rules

Easy Speedy Wikipedia Citations

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Psychologist citing literature at section ends.

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Formats academic references to specific styles.

An assistant for researching, structuring, and enhancing essays.

Generate bibtex for a research paper title

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