Best AI tools for< Prepare Question Dataset >

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

Qwizzard is an AI question generator tool that helps users create questions automatically. By enabling JavaScript, users can generate a variety of questions for quizzes, exams, or assessments. The tool simplifies the process of question creation by leveraging AI technology to provide a quick and efficient solution. With Qwizzard, users can save time and effort in developing question sets for educational purposes or knowledge assessments.

The Software Engineer Interview Questions Generator is an AI-powered tool that helps software engineers prepare for interviews by generating a wide range of technical questions related to various programming languages, frameworks, databases, and cloud services. Users can select specific topics of interest and customize the number of questions to generate. The tool leverages advanced natural language processing capabilities to provide relevant and challenging interview questions, making it a valuable resource for job seekers in the tech industry.

Eklavvya.AI is an AI-powered platform offering practice tests, practice interviews, and exam question papers with answers. It provides a comprehensive solution for exam preparation by leveraging cutting-edge technology to personalize education and revolutionize the studying process. The platform offers a wide range of practice exams, school exams, and CET exams for various subjects and courses. Additionally, it features multilingual tests, detailed performance analytics, and walkthroughs for better learning outcomes.

Acedit is an AI-powered interview coaching tool designed to assist job seekers in preparing for job interviews. It offers real-time question detection, personalized answer suggestions, interview preparation with role-specific questions, and confidence-boosting features. Acedit aims to empower users by providing AI-generated responses tailored to their experience and job requirements, helping them feel more prepared and confident during interviews.

Socratic is an AI-powered learning tool that provides students with personalized support in various subjects, including Science, Math, Literature, and Social Studies. It utilizes text and speech recognition to surface relevant learning resources and offers visual explanations of important concepts. Socratic is highly regarded by both teachers and students for its ability to clarify complex topics and supplement classroom learning.

PrepAI is an advanced test generator that uses AI to help educators create high-quality assessments quickly and easily. With PrepAI, teachers can save time, engage students with unique questions, and prepare them for success. PrepAI offers a variety of features to make test creation easy, including multiple content input options, various question formats, and an easy-to-use dashboard. PrepAI also offers a variety of advantages for educators, including the ability to analyze higher-order thinking skills, conduct tests effortlessly, and access unlimited question sets.

Interview Igniter is an AI-powered platform that provides job seekers with a robust interview simulation to fine-tune their skills, adapt to their learning curve, and get detailed feedback. It offers a comprehensive question bank, including industry-specific questions and actual interview questions asked by leading tech companies like Google, Facebook, Apple, and Amazon. Interview Igniter also provides a coding interview tool for practicing and improving coding skills, with interactive guidance and tailored learning experiences. The platform utilizes Conversation Intelligence tools for analyzing communication in real-time and providing nuanced feedback. Interview Igniter was created by Vidal Graupera, a former engineering manager at LinkedIn and Uber with over 20 years of experience hiring.

CasperPractice is an AI-powered toolkit designed to help individuals prepare for the Casper test, an open-response situational judgment test used for admissions to various professional schools. The platform offers personalized practice scenarios, instant quartile grading, detailed feedback, video answers, and a fully simulated test environment. With a large question bank and unlimited practice options, users can improve their test-taking skills and aim for a top quartile score. CasperPractice is known for its affordability, instant feedback, and realistic simulation, making it a valuable resource for anyone preparing for the Casper test.

DiscoveryAi is an AI-powered research assistant that helps you find and organize the information you need to make better decisions. With DiscoveryAi, you can quickly and easily search through a vast database of articles, news, and other content to find the information you need. You can also use DiscoveryAi to organize your research into folders and notes, and to collaborate with others on research projects.

Interviews Chat is an AI-powered interview preparation tool that offers real-time suggestions, personalized question preparation, and in-depth feedback to help users ace their interviews. The tool includes a Copilot feature with vision capability for coding challenges and whiteboard tasks. Users can practice answering questions via a video interface, receive tailored interview questions based on their resume and job description, and get instant feedback on their responses. Interviews Chat aims to provide a realistic practice environment to improve interview performance and make a lasting impression.

Neural Consult is a cutting-edge AI-powered medical education platform that offers personalized learning tools to empower medical students. The platform provides unlimited board questions, Anki cards, clinical simulations, and more, tailored to individual study needs. With features like question generation, lecture summaries, differential diagnosis, and AI-powered case simulations, Neural Consult revolutionizes the way medical students learn and prepare for exams. Trusted by students at top medical schools, Neural Consult aims to enhance clinical reasoning, memorization skills, and overall learning efficiency through innovative AI technology.

Codei is an AI-powered platform designed to help individuals land their dream software engineering job. It offers features such as application tracking, question generation, and code evaluation to assist users in honing their technical skills and preparing for interviews. Codei aims to provide personalized support and insights to help users succeed in the tech industry.

Final Round AI is an AI-powered platform designed to assist candidates in excelling during interviews and job application processes. It offers a suite of AI tools, including Interview Copilot for real-time guidance, AI Resume Builder for creating hireable resumes, and AI Mock Interview for immersive interview practice. The platform provides personalized support, industry-specific scenarios, real-time feedback, and verified question databases to help users master their interviews and stand out to potential employers.

Hirezen is an AI-powered interviewing tool designed to simplify the interview process for interviewers and hiring teams. It generates tailored interview plans with questions, helps in leading better conversations, identifies critical traits during interviews, assists in crafting meaningful feedback, and empowers users to become better interviewers. The tool aims to streamline the interviewing process and enhance the overall candidate experience.

Vibeo.ai is a powerful AI-driven tool designed to help businesses collect and utilize engaging video testimonials effortlessly. It addresses the common challenges faced by businesses, such as high visitor bounce rates, inefficient ad spend, and negative reviews. By leveraging AI technology, Vibeo.ai enables users to create campaigns, prepare questions, share links with customers, and get video testimonials edited seamlessly. The tool offers both free and pro plans with various features to cater to different business needs. With Vibeo.ai, businesses can boost their credibility, increase conversions, and maximize growth opportunities.

PrepPro is an AI-powered interview preparation tool designed to help users ace their job interviews. It offers comprehensive interview preparation resources to boost confidence and improve performance during interviews. With a user-friendly interface and structured approach, PrepPro aims to assist individuals in mastering technical questions, enhancing problem-solving skills, and boosting confidence for behavioral interviews. The tool provides self-interview practice, access to AI tools, and unlimited generations to support users in securing their dream job offers.

AskLegal.bot is a free AI-powered legal assistant that provides instant answers to your legal questions. With our proprietary AI technology, we synthesize information from thousands of sources to offer tailored guidance that aligns with current laws and regulations. Our self-service document review tool helps you understand various legal documents, including rental agreements, employment contracts, service agreements, insurance policies, and more. AskLegal.bot is confidential and secure, ensuring the privacy of your discussions with top-tier data security and privacy measures.

JobInterview.guru is an AI-powered platform designed to provide personalized interview training for job seekers. Leveraging advanced AI technology, the platform offers realistic job interview simulations, detailed insights into interview questions, and personalized feedback to help users prepare effectively. With a focus on efficiency and cost-effectiveness, JobInterview.guru aims to empower users to confidently navigate their job interviews and land their dream jobs.

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.

ReST-MCTS is a reinforced self-training approach that integrates process reward guidance with tree search MCTS to collect higher-quality reasoning traces and per-step value for training policy and reward models. It eliminates the need for manual per-step annotation by estimating the probability of steps leading to correct answers. The inferred rewards refine the process reward model and aid in selecting high-quality traces for policy model self-training.

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.

BlendSQL is a superset of SQLite designed for problem decomposition and hybrid question-answering with Large Language Models (LLMs). It allows users to blend operations over heterogeneous data sources like tables, text, and images, combining the structured and interpretable reasoning of SQL with the generalizable reasoning of LLMs. Users can oversee all calls (LLM + SQL) within a unified query language, enabling tasks such as building LLM chatbots for travel planning and answering complex questions by injecting 'ingredients' as callable functions.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

MathVerse is an all-around visual math benchmark designed to evaluate the capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving. It collects high-quality math problems with diagrams to assess how well MLLMs can understand visual diagrams for mathematical reasoning. The benchmark includes 2,612 problems transformed into six versions each, contributing to 15K test samples. It also introduces a Chain-of-Thought (CoT) Evaluation strategy for fine-grained assessment of output answers.

Nejumi Leaderboard 3 is a comprehensive evaluation platform for large language models, assessing general language capabilities and alignment aspects. The evaluation framework includes metrics for language processing, translation, summarization, information extraction, reasoning, mathematical reasoning, entity extraction, knowledge/question answering, English, semantic analysis, syntactic analysis, alignment, ethics/moral, toxicity, bias, truthfulness, and robustness. The repository provides an implementation guide for environment setup, dataset preparation, configuration, model configurations, and chat template creation. Users can run evaluation processes using specified configuration files and log results to the Weights & Biases project.

The GFM-RAG is a graph foundation model-powered pipeline that combines graph neural networks to reason over knowledge graphs and retrieve relevant documents for question answering. It features a knowledge graph index, efficiency in multi-hop reasoning, generalizability to unseen datasets, transferability for fine-tuning, compatibility with agent-based frameworks, and interpretability of reasoning paths. The tool can be used for conducting retrieval and question answering tasks using pre-trained models or fine-tuning on custom datasets.

Home LLM is a project that provides the necessary components to control your Home Assistant installation with a completely local Large Language Model acting as a personal assistant. The goal is to provide a drop-in solution to be used as a "conversation agent" component by Home Assistant. The 2 main pieces of this solution are Home LLM and Llama Conversation. Home LLM is a fine-tuning of the Phi model series from Microsoft and the StableLM model series from StabilityAI. The model is able to control devices in the user's house as well as perform basic question and answering. The fine-tuning dataset is a custom synthetic dataset designed to teach the model function calling based on the device information in the context. Llama Conversation is a custom component that exposes the locally running LLM as a "conversation agent" in Home Assistant. This component can be interacted with in a few ways: using a chat interface, integrating with Speech-to-Text and Text-to-Speech addons, or running the oobabooga/text-generation-webui project to provide access to the LLM via an API interface.

Video-MME is the first-ever comprehensive evaluation benchmark of Multi-modal Large Language Models (MLLMs) in Video Analysis. It assesses the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. The dataset comprises 900 videos with 256 hours and 2,700 human-annotated question-answer pairs. It distinguishes itself through features like duration variety, diversity in video types, breadth in data modalities, and quality in annotations.

Dataformer is a robust framework for creating high-quality synthetic datasets for AI, offering speed, reliability, and scalability. It empowers engineers to rapidly generate diverse datasets grounded in proven research methodologies, enabling them to prioritize data excellence and achieve superior standards for AI models. Dataformer integrates with multiple LLM providers using one unified API, allowing parallel async API calls and caching responses to minimize redundant calls and reduce operational expenses. Leveraging state-of-the-art research papers, Dataformer enables users to generate synthetic data with adaptability, scalability, and resilience, shifting their focus from infrastructure concerns to refining data and enhancing models.

Apollo is a multilingual medical LLM that covers English, Chinese, French, Hindi, Spanish, Hindi, and Arabic. It is designed to democratize medical AI to 6B people. Apollo has achieved state-of-the-art results on a variety of medical NLP tasks, including question answering, medical dialogue generation, and medical text classification. Apollo is easy to use and can be integrated into a variety of applications, making it a valuable tool for healthcare professionals and researchers.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

MMOS (Mix of Minimal Optimal Sets) is a dataset designed for math reasoning tasks, offering higher performance and lower construction costs. It includes various models and data subsets for tasks like arithmetic reasoning and math word problem solving. The dataset is used to identify minimal optimal sets through reasoning paths and statistical analysis, with a focus on QA-pairs generated from open-source datasets. MMOS also provides an auto problem generator for testing model robustness and scripts for training and inference.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

LL3DA is a Large Language 3D Assistant that responds to both visual and textual interactions within complex 3D environments. It aims to help Large Multimodal Models (LMM) comprehend, reason, and plan in diverse 3D scenes by directly taking point cloud input and responding to textual instructions and visual prompts. LL3DA achieves remarkable results in 3D Dense Captioning and 3D Question Answering, surpassing various 3D vision-language models. The code is fully released, allowing users to train customized models and work with pre-trained weights. The tool supports training with different LLM backends and provides scripts for tuning and evaluating models on various tasks.

InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM. It is a vision-language foundation model that can perform various tasks, including: **Visual Perception** - Linear-Probe Image Classification - Semantic Segmentation - Zero-Shot Image Classification - Multilingual Zero-Shot Image Classification - Zero-Shot Video Classification **Cross-Modal Retrieval** - English Zero-Shot Image-Text Retrieval - Chinese Zero-Shot Image-Text Retrieval - Multilingual Zero-Shot Image-Text Retrieval on XTD **Multimodal Dialogue** - Zero-Shot Image Captioning - Multimodal Benchmarks with Frozen LLM - Multimodal Benchmarks with Trainable LLM - Tiny LVLM InternVL has been shown to achieve state-of-the-art results on a variety of benchmarks. For example, on the MMMU image classification benchmark, InternVL achieves a top-1 accuracy of 51.6%, which is higher than GPT-4V and Gemini Pro. On the DocVQA question answering benchmark, InternVL achieves a score of 82.2%, which is also higher than GPT-4V and Gemini Pro. InternVL is open-sourced and available on Hugging Face. It can be used for a variety of applications, including image classification, object detection, semantic segmentation, image captioning, and question answering.

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.

DriveLM is a multimodal AI model that enables autonomous driving by combining computer vision and natural language processing. It is designed to understand and respond to complex driving scenarios using visual and textual information. DriveLM can perform various tasks related to driving, such as object detection, lane keeping, and decision-making. It is trained on a massive dataset of images and text, which allows it to learn the relationships between visual cues and driving actions. DriveLM is a powerful tool that can help to improve the safety and efficiency of autonomous vehicles.

Psychiatry board exam question bank with varied difficulty levels and explanations.

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