Best AI tools for< Study Llm >

StudyFetch is a revolutionary new platform that allows you to upload your course materials and create interactive study sets. You can study with an AI tutor, create flashcards, generate notes, take practice tests, and more. StudyFetch's AI, Spark.e, utilizes advanced machine learning algorithms to offer a tailored, interactive tutoring experience. Once you upload your study materials, Spark.e scans and indexes them, making the content searchable and accessible for real-time queries.

Study Plannr is an AI-powered application that generates personalized and smart study plans using ChatGPT. It optimizes revision by considering subjects, topics, and assessment dates. The tool is designed to help parents, educators, K12 students, and learners studying for certifications in creating customized study plans to improve learning efficiency and goal setting.

Interview.study is an AI-powered interview preparation platform that helps candidates practice real interview questions asked by top companies. The platform provides users with instant feedback on their responses, helping them identify areas for improvement and develop stronger answers. Interview.study also offers a variety of features to help candidates prepare for their interviews, including a database of interview questions, a mock interview tool, and a resume builder.

Hebrew Bible Study is a website dedicated to providing resources for studying the Hebrew Bible. Users can access various features such as unlocking pro features, asking Rabbi Ari questions, and customizing their study plans. The website is designed to help users deepen their understanding of Judaism through the study of the Hebrew Bible. It is created by Cohen Apps and based in Jerusalem.

Sara is an AI-powered platform that serves as a virtual tutor and exam planner. It leverages artificial intelligence to provide personalized learning experiences and help students prepare for exams efficiently. With Sara, users can access study materials, receive customized study plans, track their progress, and get feedback on their performance. The platform aims to enhance learning outcomes by offering adaptive learning solutions tailored to individual needs.

Course Hero is an online learning platform that provides students with access to a library of study materials, including textbooks, lecture notes, practice questions, and expert help. The platform uses AI to power its search engine and provide personalized recommendations to students. Course Hero also offers a subscription service that gives students access to additional features, such as the ability to download documents and get help from live tutors.

Quizgecko is an AI-powered platform that helps users create quizzes, flashcards, and assessments from existing content. It utilizes AI technology to generate questions and answers, offers spaced repetition for optimized learning, provides personalized feedback through an AI tutor, and allows users to study smarter with a mobile app. The platform is suitable for students, educators, and businesses looking to enhance learning and evaluation processes.

Memo is an AI-powered tool designed to transform course materials into instant notes, flashcards, and quizzes in seconds. It automates flashcard creation, offers personalized learning experiences, and saves valuable study time. Trusted by educators and learners worldwide, Memo covers 24 diverse disciplines and supports over 100 languages. The tool is built by medical students to streamline the study process, enhance learning experiences, and improve study efficiency.

OmniSets is an ultimate flashcard tool that helps students learn efficiently. It combines advanced machine learning algorithms and incorporates them throughout the platform. OmniSets offers various modes, from spaced repetition to matching games and AI tools, to make studying easier. Users can create and organize study sets, search from over 250k+ public study sets, and take advantage of the spell tool to write terms and definitions. OmniSets is a community-driven platform that values user feedback and is constantly updating and improving its features.

KardsAI is an AI-powered flashcard maker application that aims to make learning easier and more efficient. It allows users to transform any PDF, text, note, or prompt into flashcards in a snap. With features like converting PDFs to flashcards, generating flashcards from prompts, and cross-platform access, KardsAI caters to students, language learners, knowledge seekers, and trivia enthusiasts. The application saves valuable time through AI-powered flashcard creation and helps users remember information longer with a spaced repetition algorithm. KardsAI offers a freemium model, allowing users to use the app for free or upgrade for additional features at an affordable price.

KardsAI is an AI-powered flashcard maker mobile app designed to help users study smarter and more efficiently. It allows users to transform any PDF, text, note, or prompt into flashcards in a snap. With advanced AI features and a mobile-first approach, KardsAI offers exceptional quality and unique features for personalized learning experiences. The app saves valuable time by automating flashcard creation and incorporates a spaced repetition algorithm to help users remember information longer. KardsAI is suitable for students, language learners, knowledge seekers, and trivia enthusiasts, offering a wide range of functionalities to enhance learning and memory retention.

Synaptiq Learning is an AI-powered learning platform designed for medicine students to study efficiently and effectively. It offers trustworthy, publisher-grade content such as flashcards, practice tests, and free-response questions sourced from reliable medical education materials. The platform utilizes cutting-edge algorithms like spaced repetition to optimize personalized review schedules. Synaptiq is trusted in academia and aims to enhance the learning experience by considering the science behind learning processes.

Studygenie is an AI-powered learning platform that helps students study for tests 10x faster. It uses AI to generate personalized quizzes, track retention, and provide insightful explanations. Studygenie is simple to use and trusted by students at top universities.

LectoMate is an AI-driven study companion that transforms lecture files into comprehensive study materials such as key points, study guides, mind maps, flashcards, and tailored questions. It streamlines the educational journey for both students and educators by harnessing the power of advanced AI to enhance teaching and learning in mere minutes. LectoMate allows users to effortlessly generate customized study materials from their lecture notes through an intuitive platform.

The US Citizenship Practice Exam is a website designed to help users study for the US naturalization test. The site provides a practice exam with 100 questions, graded by an AI created by OpenAI. Users need to answer 6 out of 10 questions correctly to pass the actual test, which is an oral test graded by a USCIS officer. The website is created by Evan Conrad and is open source on Github. Users can find the full list of questions and rules on the site.

Axon is an AI-powered study and test prep platform designed specifically for the USMLE exam. It offers a comprehensive and personalized approach to help students study smarter, not harder. With a focus on efficiency and effectiveness, Axon aims to enhance the learning experience for medical students preparing for their exams. The platform provides a range of study resources, practice questions, and personalized study plans to optimize exam preparation and boost performance.

CheatGPT is an AI-powered virtual study assistant designed to help students with their academic tasks. It offers a wide range of features such as providing fast and concise answers, image recognition capabilities, personal tutoring in various subjects, translations, and grammar corrections. The application utilizes advanced AI models like GPT-4o and Claude 3.5 Sonnet to deliver accurate and efficient responses to user queries. CheatGPT aims to enhance the learning experience for students by offering a cost-effective and efficient alternative to traditional study methods.

Monic.ai is an all-in-one AI-powered study platform that revolutionizes the way students learn. It offers a suite of tools for creating personalized study materials, including quizzes, flashcards, and summaries, all aimed at enhancing learning efficiency. The platform uses advanced AI to provide tailored learning experiences, making study sessions more effective and engaging. Monic.ai caters to students at various academic levels, educators, and professionals looking to upskill, offering tools and support to achieve their learning goals.

LearnMate is a revolutionary AI-integrated productivity app designed for students. It offers collaborative tools for study goal setting, assignment and task management, note-taking, and more. The app utilizes cutting-edge AI technology to enhance the learning experience and productivity of students. LearnMate is accessible on various devices, allowing seamless progress tracking and data synchronization across PCs, tablets, and mobile devices. With features like AI tutor assistance, assignment management, task lists, study notes creation, and more, LearnMate aims to revolutionize the way students study and excel in their academic endeavors.

TutorEva is an AI-powered homework helper and tutor designed to assist college students with various subjects. It offers features such as AI homework solving, essay writing, course material integration, problem-solving, and personalized tutoring. With cutting-edge AI technology, TutorEva provides accurate solutions and explanations to help students excel in their academic endeavors.

LLM-PLSE-paper is a repository focused on the applications of Large Language Models (LLMs) in Programming Language and Software Engineering (PL/SE) domains. It covers a wide range of topics including bug detection, specification inference and verification, code generation, fuzzing and testing, code model and reasoning, code understanding, IDE technologies, prompting for reasoning tasks, and agent/tool usage and planning. The repository provides a comprehensive collection of research papers, benchmarks, empirical studies, and frameworks related to the capabilities of LLMs in various PL/SE tasks.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

This repository contains the source code and datasets for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs accepted to ACL 2024. It proposes a framework called Graph Chain-of-thought (Graph-CoT) to enable Language Models to traverse graphs step-by-step for reasoning, interaction, and execution. The motivation is to alleviate hallucination issues in Language Models by augmenting them with structured knowledge sources represented as graphs.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.

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.

Awesome-llm-role-playing-with-persona is a curated list of resources for large language models for role-playing with assigned personas. It includes papers and resources related to persona-based dialogue systems, personalized response generation, psychology of LLMs, biases in LLMs, and more. The repository aims to provide a comprehensive collection of research papers and tools for exploring role-playing abilities of large language models in various contexts.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

Awesome LLM Courses is a curated list of online courses focused on Large Language Models (LLMs). The repository aims to provide a comprehensive collection of free available courses covering various aspects of LLMs, including fundamentals, engineering, and applications. The courses are suitable for individuals interested in natural language processing, AI development, and machine learning. The list includes courses from reputable platforms such as Hugging Face, Udacity, DeepLearning.AI, Cohere, DataCamp, and more, offering a wide range of topics from pretraining LLMs to building AI applications with LLMs. Whether you are a beginner looking to understand the basics of LLMs or an intermediate developer interested in advanced topics like prompt engineering and generative AI, this repository has something for everyone.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

This is the official repository for the ACM CCS 2024 paper 'Do Anything Now': Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. The project employs a new framework called JailbreakHub to conduct the first measurement study on jailbreak prompts in the wild, collecting 15,140 prompts from December 2022 to December 2023, including 1,405 jailbreak prompts. The dataset serves as the largest collection of in-the-wild jailbreak prompts. The repository contains examples of harmful language and is intended for research purposes only.

Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. This repository conducts a comprehensive survey study on the construction, application, and evaluation of LLM-based autonomous agents. It explores essential components of AI agents, application domains in natural sciences, social sciences, and engineering, and evaluation strategies. The survey aims to be a resource for researchers and practitioners in this rapidly evolving field.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

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.

LLM.swift is a simple and readable library that allows you to interact with large language models locally with ease for macOS, iOS, watchOS, tvOS, and visionOS. It's a lightweight abstraction layer over `llama.cpp` package, so that it stays as performant as possible while is always up to date. Theoretically, any model that works on `llama.cpp` should work with this library as well. It's only a single file library, so you can copy, study and modify the code however you want.

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