Best AI tools for< Cite Library >

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.

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.

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.

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 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.

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.

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.

Afforai is a powerful AI research assistant and chatbot that serves as an AI-powered reference manager for researchers. It helps manage, annotate, cite papers, and conduct literature reviews with AI reliably. With features like managing research papers, annotating and highlighting notes, managing citations and metadata, collaborating on notes, and supporting various document formats, Afforai streamlines academic workflows and enhances research productivity. Trusted by over 50,000 researchers worldwide, Afforai offers advanced AI capabilities, including GPT-4 and Claude 3.5 Sonnet, along with secure data handling and seamless integrations.

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.

The Ollama R library provides an easy way to integrate R with Ollama for running language models locally on your machine. It supports working with standard data structures for different LLMs, offers various output formats, and enables integration with other libraries/tools. The library uses the Ollama REST API and requires the Ollama app to be installed, with GPU support for accelerating LLM inference. It is inspired by Ollama Python and JavaScript libraries, making it familiar for users of those languages. The installation process involves downloading the Ollama app, installing the 'ollamar' package, and starting the local server. Example usage includes testing connection, downloading models, generating responses, and listing available models.

Baal is an active learning library that supports both industrial applications and research use cases. It provides a framework for Bayesian active learning methods such as Monte-Carlo Dropout, MCDropConnect, Deep ensembles, and Semi-supervised learning. Baal helps in labeling the most uncertain items in the dataset pool to improve model performance and reduce annotation effort. The library is actively maintained by a dedicated team and has been used in various research papers for production and experimentation.

Inspectus is a versatile visualization tool for large language models. It provides multiple views, including Attention Matrix, Query Token Heatmap, Key Token Heatmap, and Dimension Heatmap, to offer insights into language model behaviors. Users can interact with the tool in Jupyter notebooks through an easy-to-use Python API. Inspectus allows users to visualize attention scores between tokens, analyze how tokens focus on each other during processing, and explore the relationships between query and key tokens. The tool supports the visualization of attention maps from Huggingface transformers and custom attention maps, making it a valuable resource for researchers and developers working with language models.

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.

Hezar is an all-in-one AI library designed specifically for the Persian community. It brings together various AI models and tools, making it easy to use AI with just a few lines of code. The library seamlessly integrates with Hugging Face Hub, offering a developer-friendly interface and task-based model interface. In addition to models, Hezar provides tools like word embeddings, tokenizers, feature extractors, and more. It also includes supplementary ML tools for deployment, benchmarking, and optimization.

AixLib is a Modelica model library for building performance simulations developed at RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate (EBC) in Aachen, Germany. It contains models of HVAC systems as well as high and reduced order building models. The name AixLib is derived from the city's French name Aix-la-Chapelle, following a local tradition. The library is continuously improved and offers citable papers for reference. Contributions to the development can be made via Issues section or Pull Requests, following the workflow described in the Wiki. AixLib is released under a 3-clause BSD-license with acknowledgements to public funded projects and financial support by BMWi (German Federal Ministry for Economic Affairs and Energy).

TinyTroupe is an experimental Python library that leverages Large Language Models (LLMs) to simulate artificial agents called TinyPersons with specific personalities, interests, and goals in simulated environments. The focus is on understanding human behavior through convincing interactions and customizable personas for various applications like advertisement evaluation, software testing, data generation, project management, and brainstorming. The tool aims to enhance human imagination and provide insights for better decision-making in business and productivity scenarios.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

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.

skpro is a library for supervised probabilistic prediction in python. It provides `scikit-learn`-like, `scikit-base` compatible interfaces to: * tabular **supervised regressors for probabilistic prediction** \- interval, quantile and distribution predictions * tabular **probabilistic time-to-event and survival prediction** \- instance-individual survival distributions * **metrics to evaluate probabilistic predictions** , e.g., pinball loss, empirical coverage, CRPS, survival losses * **reductions** to turn `scikit-learn` regressors into probabilistic `skpro` regressors, such as bootstrap or conformal * building **pipelines and composite models** , including tuning via probabilistic performance metrics * symbolic **probability distributions** with value domain of `pandas.DataFrame`-s and `pandas`-like interface

Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks.

BitBLAS is a library for mixed-precision BLAS operations on GPUs, for example, the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication where $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$. BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs), for example, the $W_{UINT4}A_{FP16}$ in GPTQ, the $W_{INT2}A_{FP16}$ in BitDistiller, the $W_{INT2}A_{INT8}$ in BitNet-b1.58. BitBLAS is based on techniques from our accepted submission at OSDI'24.

cuVS is a library that contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

Unitxt is a customizable library for textual data preparation and evaluation tailored to generative language models. It natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively.

aiocoap is a Python library that implements the Constrained Application Protocol (CoAP) using native asyncio methods in Python 3. It supports various CoAP standards such as RFC7252, RFC7641, RFC7959, RFC8323, RFC7967, RFC8132, RFC9176, RFC8613, and draft-ietf-core-oscore-groupcomm-17. The library provides features for clients and servers, including multicast support, blockwise transfer, CoAP over TCP, TLS, and WebSockets, No-Response, PATCH/FETCH, OSCORE, and Group OSCORE. It offers an easy-to-use interface for concurrent operations and is suitable for IoT applications.

LLMBox is a comprehensive library designed for implementing Large Language Models (LLMs) with a focus on a unified training pipeline and comprehensive model evaluation. It serves as a one-stop solution for training and utilizing LLMs, offering flexibility and efficiency in both training and utilization stages. The library supports diverse training strategies, comprehensive datasets, tokenizer vocabulary merging, data construction strategies, parameter efficient fine-tuning, and efficient training methods. For utilization, LLMBox provides comprehensive evaluation on various datasets, in-context learning strategies, chain-of-thought evaluation, evaluation methods, prefix caching for faster inference, support for specific LLM models like vLLM and Flash Attention, and quantization options. The tool is suitable for researchers and developers working with LLMs for natural language processing tasks.

The Classical Language Toolkit (CLTK) is a Python library that provides natural language processing (NLP) capabilities for pre-modern languages. It offers a modular processing pipeline with pre-configured defaults and supports almost 20 languages. Users can install the latest version using pip and access detailed documentation on the official website. The toolkit is designed to meet the unique needs of researchers working with historical languages, filling a void in the NLP landscape that often neglects non-spoken languages and different research goals.

The Universal Numbers Library is a header-only C++ template library designed for universal number arithmetic, offering alternatives to native integer and floating-point for mixed-precision algorithm development and optimization. It tailors arithmetic types to the application's precision and dynamic range, enabling improved application performance and energy efficiency. The library provides fast implementations of special IEEE-754 formats like quarter precision, half-precision, and quad precision, as well as vendor-specific extensions. It supports static and elastic integers, decimals, fixed-points, rationals, linear floats, tapered floats, logarithmic, interval, and adaptive-precision integers, rationals, and floats. The library is suitable for AI, DSP, HPC, and HFT algorithms.

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.

T-MAC is a kernel library that directly supports mixed-precision matrix multiplication without the need for dequantization by utilizing lookup tables. It aims to boost low-bit LLM inference on CPUs by offering support for various low-bit models. T-MAC achieves significant speedup compared to SOTA CPU low-bit framework (llama.cpp) and can even perform well on lower-end devices like Raspberry Pi 5. The tool demonstrates superior performance over existing low-bit GEMM kernels on CPU, reduces power consumption, and provides energy savings. It achieves comparable performance to CUDA GPU on certain tasks while delivering considerable power and energy savings. T-MAC's method involves using lookup tables to support mpGEMM and employs key techniques like precomputing partial sums, shift and accumulate operations, and utilizing tbl/pshuf instructions for fast table lookup.

Expert in all academic in-text citation styles.

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Formats academic references to specific styles.

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