Best AI tools for< Prepare Question Dataset >

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.

The Software Engineer Interview Questions Generator is an AI tool that helps software engineers prepare for interviews by generating a wide range of technical questions based on various programming languages, frameworks, and technologies. Users can select specific topics and the number of questions they want to generate, making it a valuable resource for interview preparation. The tool leverages AI technology to provide relevant and challenging questions that cover a diverse set of topics commonly encountered in software engineering 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.

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.

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.

Spective is an AI-powered Entrepreneurial Guidance platform that offers AI-Business Coaching Modules to help entrepreneurs perfect their pitch, validate business ideas, and plan their next steps. The platform combines AI-generated tools like SWOT analysis and Pitch Deck Slides with human insights from experienced business coaches, accelerators, and investors to provide personalized guidance and support to entrepreneurs. Spective aims to revolutionize entrepreneurial guidance by unlocking dreams and fostering sustainable business development through the power of AI.

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.

AI Interview Answers Generator is an innovative tool designed to assist individuals in acing their job interviews by providing real-time voice transcription, instant optimal solutions, and industry-specific knowledge base. The tool acts as a virtual copilot during interviews, ensuring users have access to relevant and up-to-date information to stand out among other candidates. With cutting-edge AI technology, users can confidently navigate through technical questions and showcase their skills effectively.

Pgrammer is an AI-powered platform designed to help users practice coding interview questions with hints and personalized learning experiences. Unlike traditional methods like LeetCode, Pgrammer offers a diverse set of questions for over 20 programming languages, real-time hints, and solution analysis to improve coding skills and knowledge. The platform provides feedback on submitted solutions, helping users ace coding interviews with confidence.

MasterInterview is your ultimate AI interview coach, designed to help you master job-specific interviews through interactive sessions. Backed by industry experts and powered by advanced GPT-4 technology, the platform offers personalized feedback, tailored interview questions, and virtual interview sessions with a fox avatar. With comprehensive feedback and revised answers, MasterInterview aims to assist candidates in landing their dream jobs effectively and efficiently.

PrepMasterAI is an AI-powered platform designed to help individuals ace their job interviews by providing personalized practice questions, real-time feedback, and performance tracking. Users can unlock their full potential through tailored practice questions and insights to improve their interview skills. The platform aims to assist job seekers in identifying and enhancing their strengths and weaknesses to increase their chances of landing their dream job.

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.

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.

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.

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.

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.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

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.

MME-RealWorld is a benchmark designed to address real-world applications with practical relevance, featuring 13,366 high-resolution images and 29,429 annotations across 43 tasks. It aims to provide substantial recognition challenges and overcome common barriers in existing Multimodal Large Language Model benchmarks, such as small data scale, restricted data quality, and insufficient task difficulty. The dataset offers advantages in data scale, data quality, task difficulty, and real-world utility compared to existing benchmarks. It also includes a Chinese version with additional images and QA pairs focused on Chinese scenarios.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

Habitat-Sim is a high-performance physics-enabled 3D simulator with support for 3D scans of indoor/outdoor spaces, CAD models of spaces and piecewise-rigid objects, configurable sensors, robots described via URDF, and rigid-body mechanics. It prioritizes simulation speed over the breadth of simulation capabilities, achieving several thousand frames per second (FPS) running single-threaded and over 10,000 FPS multi-process on a single GPU when rendering a scene from the Matterport3D dataset. Habitat-Sim simulates a Fetch robot interacting in ReplicaCAD scenes at over 8,000 steps per second (SPS), where each ‘step’ involves rendering 1 RGBD observation (128×128 pixels) and rigid-body dynamics for 1/30sec.

MotionLLM is a framework for human behavior understanding that leverages Large Language Models (LLMs) to jointly model videos and motion sequences. It provides a unified training strategy, dataset MoVid, and MoVid-Bench for evaluating human behavior comprehension. The framework excels in captioning, spatial-temporal comprehension, and reasoning abilities.

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