Best AI tools for< Cite >

ChatDOC is an AI-powered tool that allows users to chat with PDF documents and get instant answers with cited sources. It can summarize long documents, explain complex concepts, and find key information in seconds. ChatDOC is built for professionals and is used by over 500,000 global users.

Upwork Proposals Generator is an AI tool designed to help freelancers on Upwork create unique and personalized proposals quickly and efficiently. Unlike traditional tools, it uses AI technology to tailor proposals based on the freelancer's profile and the client's requirements. The tool focuses on speed, quality, and quantity of proposals to increase the freelancer's chances of winning contracts on Upwork. With features like local references, greetings in the client's language, and customizable styles, the tool aims to streamline the proposal writing process and make freelancers stand out in the competitive Upwork marketplace.

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.

Innhold.ai is an AI-powered writing assistant designed to enhance your writing experience. It offers advanced features such as grammar checking, sentence restructuring, and vocabulary enhancement to help you create high-quality content. With its intuitive interface, users can easily improve their writing skills and productivity. Whether you are a student, professional writer, or content creator, Innhold.ai can assist you in crafting engaging and error-free text.

Stylus is an AI-powered essay writing tool designed to assist students in creating academic papers. It offers features such as AI-driven editor, paper generator, and personal statement writer. Stylus provides comprehensive and well-structured writing, uses reliable sources, and ensures stylistic accuracy. The tool is multilingual, works on mobile devices, and guarantees original content without plagiarism. Users can benefit from the AI's ability to understand personal writing style and voice, making the writing process efficient and insightful.

Medical Chat is an advanced AI assistant designed for healthcare professionals, providing instant and accurate medical answers for both human and veterinary medicine. Its capabilities include diagnosing medical conditions, generating differential diagnosis reports, creating patient-specific clinic plans, and offering comprehensive drug information. Medical Chat utilizes the latest LLM models, including ChatGPT 3.5 and 4.0, to deliver reliable and up-to-date medical knowledge. The platform also features a vast database of professional medical textbooks, veterinary books, and PubMed articles, ensuring evidence-based responses. With its HIPAA compliance and commitment to data privacy, Medical Chat empowers healthcare providers to enhance their diagnostic capabilities and improve patient outcomes.

Chat-docs AI is an innovative AI application that allows users to interact with PDF documents through natural language conversations. The tool leverages advanced artificial intelligence algorithms to summarize long documents, explain complex concepts, and find key information with cited sources in seconds. It transforms PDFs into intelligent entities capable of dialogue, making learning, research, and analysis more interactive and personalized. Chat-docs AI is designed to be intuitive, secure, and accessible to users from various backgrounds, revolutionizing the way individuals engage with textual content.

PubCompare is a powerful AI-powered tool that helps scientists search, compare, and evaluate experimental protocols. With over 40 million protocols in its database, PubCompare is the largest repository of trusted experimental protocols. PubCompare's AI-powered search features allow users to find similar protocols, highlight critical steps, and evaluate the reproducibility of protocols based on in-protocol citations. PubCompare is available from any computer and requires no download.

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.

Genei is an AI-powered summarization and research tool that helps users with qualitative research, content production, academic writing, and professional writing. It enables users to research faster by automatically summarizing background reading and producing blogs, articles, and reports efficiently. Genei offers features such as summarization, keyword extraction, document management, annotation capabilities, and citation management. Trusted by thought leaders and experts, Genei enhances productivity, saves time, and improves the quality and efficiency of research and writing tasks.

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.

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.

JabRef is an open-source, cross-platform citation and reference management tool that helps users collect, organize, cite, and share research sources. It offers features like searching across online scientific catalogues, importing references in various formats, extracting metadata from PDFs, customizable citation key generator, support for Word and LibreOffice/OpenOffice, and more. Users can organize their research items hierarchically, find and merge duplicates, attach related documents, and keep track of what they read. JabRef also supports sharing via various export options and syncs library contents in a team via a SQL database. It is actively developed, free of charge, and offers native BibTeX and Biblatex support.

LLM-Planner is a tool for few-shot grounded planning for embodied agents using large language models. It includes a high-level prompt generator and kNN dataset, allowing users to generate high-level plans for tasks by bringing their low-level controller and an LLM. The tool has been used in various research projects and provides implementation examples from different conferences. Users can cite the tool using the provided information and the tool is available under the MIT License. For questions or issues, users can contact Luke Song.

Perplexica is an open-source AI-powered search engine that utilizes advanced machine learning algorithms to provide clear answers with sources cited. It offers various modes like Copilot Mode, Normal Mode, and Focus Modes for specific types of questions. Perplexica ensures up-to-date information by using SearxNG metasearch engine. It also features image and video search capabilities and upcoming features include finalizing Copilot Mode and adding Discover and History Saving features.

OlympicArena is a comprehensive benchmark designed to evaluate advanced AI capabilities across various disciplines. It aims to push AI towards superintelligence by tackling complex challenges in science and beyond. The repository provides detailed data for different disciplines, allows users to run inference and evaluation locally, and offers a submission platform for testing models on the test set. Additionally, it includes an annotation interface and encourages users to cite their paper if they find the code or dataset helpful.

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.

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.

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.

ActionWeaver is an AI application framework designed for simplicity, relying on OpenAI and Pydantic. It supports both OpenAI API and Azure OpenAI service. The framework allows for function calling as a core feature, extensibility to integrate any Python code, function orchestration for building complex call hierarchies, and telemetry and observability integration. Users can easily install ActionWeaver using pip and leverage its capabilities to create, invoke, and orchestrate actions with the language model. The framework also provides structured extraction using Pydantic models and allows for exception handling customization. Contributions to the project are welcome, and users are encouraged to cite ActionWeaver if found useful.

LongCite is a tool that enables Large Language Models (LLMs) to generate fine-grained citations in long-context Question Answering (QA) scenarios. It provides models trained on GLM-4-9B and Meta-Llama-3.1-8B, supporting up to 128K context. Users can deploy LongCite chatbots, generate accurate responses, and obtain precise sentence-level citations. The tool includes components for model deployment, Coarse to Fine (CoF) pipeline for data construction, model training using LongCite-45k dataset, evaluation with LongBench-Cite benchmark, and citation generation.

This repository contains a minimal, clean code implementation of the Byte Pair Encoding (BPE) algorithm, commonly used in LLM tokenization. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. This algorithm was popularized for LLMs by the GPT-2 paper and the associated GPT-2 code release from OpenAI. Sennrich et al. 2015 is cited as the original reference for the use of BPE in NLP applications. Today, all modern LLMs (e.g. GPT, Llama, Mistral) use this algorithm to train their tokenizers. There are two Tokenizers in this repository, both of which can perform the 3 primary functions of a Tokenizer: 1) train the tokenizer vocabulary and merges on a given text, 2) encode from text to tokens, 3) decode from tokens to text. The files of the repo are as follows: 1. minbpe/base.py: Implements the `Tokenizer` class, which is the base class. It contains the `train`, `encode`, and `decode` stubs, save/load functionality, and there are also a few common utility functions. This class is not meant to be used directly, but rather to be inherited from. 2. minbpe/basic.py: Implements the `BasicTokenizer`, the simplest implementation of the BPE algorithm that runs directly on text. 3. minbpe/regex.py: Implements the `RegexTokenizer` that further splits the input text by a regex pattern, which is a preprocessing stage that splits up the input text by categories (think: letters, numbers, punctuation) before tokenization. This ensures that no merges will happen across category boundaries. This was introduced in the GPT-2 paper and continues to be in use as of GPT-4. This class also handles special tokens, if any. 4. minbpe/gpt4.py: Implements the `GPT4Tokenizer`. This class is a light wrapper around the `RegexTokenizer` (2, above) that exactly reproduces the tokenization of GPT-4 in the tiktoken library. The wrapping handles some details around recovering the exact merges in the tokenizer, and the handling of some unfortunate (and likely historical?) 1-byte token permutations. Finally, the script train.py trains the two major tokenizers on the input text tests/taylorswift.txt (this is the Wikipedia entry for her kek) and saves the vocab to disk for visualization. This script runs in about 25 seconds on my (M1) MacBook. All of the files above are very short and thoroughly commented, and also contain a usage example on the bottom of the file.

A Python interface for creating battling Pokemon agents, 'poke-env' allows users to develop rule-based or Reinforcement Learning bots to battle on Pokemon Showdown. The tool provides an easy-to-use interface for agent creation and offers documentation, examples, and starting code for beginners. Users can install 'poke-env' via pip and set up a development server for testing. The project is inspired by an artificial intelligence class project and relies on data from Smogon forums' RMT section. It is licensed under MIT and can be cited using a provided BibTeX entry.

LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.

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.

RAVE is a variational autoencoder for fast and high-quality neural audio synthesis. It can be used to generate new audio samples from a given dataset, or to modify the style of existing audio samples. RAVE is easy to use and can be trained on a variety of audio datasets. It is also computationally efficient, making it suitable for real-time applications.

This repository hosts the Machine Learning for Games Course, providing mdx files and notebooks for learning. The course covers various topics related to applying machine learning techniques in game development. It offers a syllabus and resources for users to sign up and access the content for free. The project is maintained by Thomas Simonini and is available on GitHub for citation in publications.

This repository contains the code for the paper Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. It demonstrates how finetuned LLMs can be used to generate stable materials, match or exceed the performance of domain specific models, mutate existing materials, and sample crystal structures conditioned on text descriptions. The method is distinct from CrystaLLM, which trains language models from scratch on CIF-formatted crystals.

AIW is a code base for experiments and raw data related to Alice in Wonderland, showcasing complete reasoning breakdown in state-of-the-art large language models. Users can collect experiments data using LiteLLM and TogetherAI, and plot the data using provided scripts. The tool allows for executing experiments over LiteLLM and lmsys, with options for different prompt types and AIW variations. The project also includes acknowledgments and a citation for reference.

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.

Benchmark various LLM Structured Output frameworks like Instructor, Mirascope, Langchain, LlamaIndex, Fructose, Marvin, Outlines, LMFormatEnforcer, etc on tasks like multi-label classification, named entity recognition, synthetic data generation. The tool provides benchmark results, methodology, instructions to run the benchmark, add new data, and add a new framework. It also includes a roadmap for framework-related tasks, contribution guidelines, citation information, and feedback request.

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