Best AI tools for< Cite Research >

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

TriForce is a training-free tool designed to accelerate long sequence generation. It supports long-context Llama models and offers both on-chip and offloading capabilities. Users can achieve a 2.2x speedup on a single A100 GPU. TriForce also provides options for offloading with tensor parallelism or without it, catering to different hardware configurations. The tool includes a baseline for comparison and is optimized for performance on RTX 4090 GPUs. Users can cite the associated paper if they find TriForce useful for their projects.

FuseAI is a repository that focuses on knowledge fusion of large language models. It includes FuseChat, a state-of-the-art 7B LLM on MT-Bench, and FuseLLM, which surpasses Llama-2-7B by fusing three open-source foundation LLMs. The repository provides tech reports, releases, and datasets for FuseChat and FuseLLM, showcasing their performance and advancements in the field of chat models and large language models.

MIMIR is a Python package designed for measuring memorization in Large Language Models (LLMs). It provides functionalities for conducting experiments related to membership inference attacks on LLMs. The package includes implementations of various attacks such as Likelihood, Reference-based, Zlib Entropy, Neighborhood, Min-K% Prob, Min-K%++, Gradient Norm, and allows users to extend it by adding their own datasets and attacks.

The Hume AI Python SDK allows users to integrate Hume APIs directly into their Python applications. Users can access complete documentation, quickstart guides, and example notebooks to get started. The SDK is designed to provide support for Hume's expressive communication platform built on scientific research. Users are encouraged to create an account at beta.hume.ai and stay updated on changes through Discord. The SDK may undergo breaking changes to improve tooling and ensure reliable releases in the future.

Step-DPO is a method for enhancing long-chain reasoning ability of LLMs with a data construction pipeline creating a high-quality dataset. It significantly improves performance on math and GSM8K tasks with minimal data and training steps. The tool fine-tunes pre-trained models like Qwen2-7B-Instruct with Step-DPO, achieving superior results compared to other models. It provides scripts for training, evaluation, and deployment, along with examples and acknowledgements.

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.

Trace is a new AutoDiff-like tool for training AI systems end-to-end with general feedback. It generalizes the back-propagation algorithm by capturing and propagating an AI system's execution trace. Implemented as a PyTorch-like Python library, users can write Python code directly and use Trace primitives to optimize certain parts, similar to training neural networks.

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.

ContextCite is a tool for attributing statements generated by LLMs back to specific parts of the context. It allows users to analyze and understand the sources of information used by language models in generating responses. By providing attributions, users can gain insights into how the model makes decisions and where the information comes from.

Sarathi-Serve is the official OSDI'24 artifact submission for paper #444, focusing on 'Taming Throughput-Latency Tradeoff in LLM Inference'. It is a research prototype built on top of CUDA 12.1, designed to optimize throughput-latency tradeoff in Large Language Models (LLM) inference. The tool provides a Python environment for users to install and reproduce results from the associated experiments. Users can refer to specific folders for individual figures and are encouraged to cite the paper if they use the tool in their work.

The 'llm-past-tense' repository contains code related to the research paper 'Does Refusal Training in LLMs Generalize to the Past Tense?' by Maksym Andriushchenko and Nicolas Flammarion. It explores the generalization of refusal training in large language models (LLMs) to the past tense. The code includes experiments and examples for running different models and requests related to the study. Users can cite the work if found useful in their research, and the codebase is released under the MIT License.

TotalSegmentator is a 3D Slicer extension designed for fully automatic whole body CT segmentation using the 'TotalSegmentator' AI model. The computation time is less than one minute, making it efficient for research purposes. Users can set up GPU acceleration for faster segmentation. The tool provides a user-friendly interface for loading CT images, creating segmentations, and displaying results in 3D. Troubleshooting steps are available for common issues such as failed computation, GPU errors, and inaccurate segmentations. Contributions to the extension are welcome, following 3D Slicer contribution guidelines.

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.

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It provides a comprehensive set of tools for medical image analysis, including data preprocessing, model training, and evaluation. MONAI is designed to be flexible and easy to use, making it a valuable resource for researchers and developers in the field of medical imaging.

Awesome-LLM-in-Social-Science is a repository that compiles papers evaluating Large Language Models (LLMs) from a social science perspective. It includes papers on evaluating, aligning, and simulating LLMs, as well as enhancing tools in social science research. The repository categorizes papers based on their focus on attitudes, opinions, values, personality, morality, and more. It aims to contribute to discussions on the potential and challenges of using LLMs in social science research.

SPEAR is a Simulator for Photorealistic Embodied AI Research that addresses limitations in existing simulators by offering 300 unique virtual indoor environments with detailed geometry, photorealistic materials, and unique floor plans. It provides an OpenAI Gym interface for interaction via Python, released under an MIT License. The simulator was developed with support from the Intelligent Systems Lab at Intel and Kujiale.

Grand Challenge is a platform that provides access to large amounts of annotated training data, objective comparisons of state-of-the-art machine learning solutions, and clinical validation using real-world data. It assists researchers, data scientists, and clinicians in collaborating to develop robust machine learning solutions to problems in biomedical imaging.

This repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more. It includes monthly best GenAI papers list, interview resources, free courses, and code repositories/notebooks for developing generative AI applications. The repository is regularly updated with the latest additions to keep users informed and engaged in the field of generative AI.

OmniGibson is a platform for accelerating Embodied AI research built upon NVIDIA's Omniverse platform. It features photorealistic visuals, physical realism, fluid and soft body support, large-scale high-quality scenes and objects, dynamic kinematic and semantic object states, mobile manipulator robots with modular controllers, and an OpenAI Gym interface. The platform provides a comprehensive environment for researchers to conduct experiments and simulations in the field of Embodied AI.

Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

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