Best AI tools for< survey research >

CloudResearch is an online platform that offers tools for online research and participant recruitment. It provides academic and market researchers with immediate access to millions of diverse, high-quality respondents worldwide. The platform is designed to help researchers recruit vetted online participants, manage complex research projects, and elevate data quality. CloudResearch also offers AI-powered solutions for survey research, participant engagement, and data quality assurance.

Roundtable is an AI-assisted data cleaning tool that helps users clean survey data efficiently. It offers an easy-to-integrate API for cleaning open-ended survey responses, saving up to 70% of time. The tool is trusted by leaders and innovators in various industries for improving data quality efforts and providing reliable human-generated insights.

Prolific is a platform that connects researchers with research participants and AI taskers. It provides a pool of over 120,000 active participants who are fully vetted and verified, so researchers can be sure that they are getting high-quality data. Prolific also has a number of features that make it easy for researchers to design and manage their studies, including a user-friendly interface, a variety of question types, and support for multiple languages. With Prolific, researchers can quickly and easily find the participants they need to conduct their research.

Fillout is an AI-powered form builder that allows users to create powerful forms, surveys, and quizzes that their audience will enjoy answering. It is designed to be easy to use, with a drag-and-drop interface and a variety of templates to choose from. Fillout also integrates with a variety of other tools, such as Airtable, Salesforce, and Google Sheets, making it a versatile option for businesses of all sizes.

Feathery is an AI-powered form builder that helps you create beautiful, mobile-friendly forms in minutes. With Feathery, you can easily drag and drop form fields, add logic and conditional rules, and collect data from your users. Feathery also integrates with a variety of third-party services, making it easy to connect your forms to your other business applications.

Metaforms is an AI-powered form builder that helps businesses create personalized and engaging forms. It uses AI to automatically generate conditions, logics, and branching, which can save businesses up to 85% in setup time. Metaforms also uses AI to provide real-time question framing and acknowledgements, which can increase completion rates by up to 30%. Additionally, Metaforms' AI can ask follow-up questions based on other user responses in a cohort, which can provide businesses with more insights about user behavior, intent, and preferences.

Outset is an AI-powered research platform that enables users to conduct and synthesize video, audio, and text conversations with hundreds of participants at once. It uses AI to moderate conversations, identify common themes, tag relevant conversations, and pull out powerful quotes. Outset is designed to help researchers understand the 'why' behind answers and gain deeper insights into the people they serve.

Vocads is a conversational voice AI platform that reinvents the survey experience. It allows companies to collect richer data, improve their strategy, and retain clients through voice surveys. Vocads also provides employee voice surveys to engage employees, improve management, and upgrade company culture. The platform is easy to use with its no-code design, allowing users to create voice surveys from scratch instinctively and save templates for reuse. Vocads is GDPR compliant and offers data sovereignty, giving brands full control over their data.

Simulai is an open-source conversational form builder that allows users to create interactive surveys and forms that feel like natural conversations. It is inspired by the simplicity of Notion and is completely free to use. With Simulai, users can easily add logic, choose from a list of templates, and host their forms on their own servers or use Simulai's free cloud hosting services.

Simulai is an open-source conversational form builder that allows users to create interactive surveys and forms that feel like natural conversations. It is inspired by the simplicity of Notion and is completely free to use. With Simulai, users can easily add logic, choose from a list of templates, and host their forms on their own servers or use Simulai's free cloud hosting services.

Surveyed.live is an AI-powered video survey platform that allows businesses to collect feedback and insights from customers through customizable survey templates. The platform offers features such as video surveys, AI touch response, comprehensible dashboard, Chrome extension, actionable insights, integration, predefined library, appealing survey creation, customer experience statistics, and more. Surveyed.live helps businesses enhance customer satisfaction, improve decision-making, and drive business growth by leveraging AI technology for video reviews and surveys. The platform caters to various industries including hospitality, healthcare, education, customer service, delivery services, and more, providing a versatile solution for optimizing customer relationships and improving overall business performance.

Prolific is a platform that connects researchers with research participants and AI taskers. It offers a pool of over 120,000 active participants who are fully vetted and verified, ensuring the quality and reliability of the data collected. Prolific's platform is designed for ease of use, allowing researchers to quickly and easily create surveys, experiments, and tasks. It also provides advanced support for flexible study design, including support for text, imagery, voice, or interaction. Prolific's mission is to make diverse, high-quality data available to anyone, anywhere.

Askaiform is an AI tool that simplifies the process of creating instant feedback questions from product descriptions. It offers a streamlined solution for generating customized feedback forms effortlessly. With features like analytics, summarization, sentiment analysis, and customizable form creation, Askaiform empowers users to capture and analyze responses effectively. The platform supports multiple languages for form generation and analysis, and provides options to export data in formats like CSV and Excel, enhancing usability and accessibility for diverse user bases.

SurveyMonkey is a popular free online survey tool that offers a wide range of features to help users create, distribute, and analyze surveys. With integrations, customizable forms, AI-powered survey creation, and market research solutions, SurveyMonkey caters to various industries and roles. It provides templates for different use cases like customer satisfaction, employee feedback, and event registration. The platform also offers resources, blog posts, and customer success stories to guide users in maximizing the benefits of survey data.

Groupt is an AI-powered data categorization and analytics application that simplifies the process of transforming complex arrays of qualitative data into clear, actionable insights for enhanced decision-making. Users can upload CSV files containing data such as user feedback, survey responses, or any qualitative data to receive visualizations of response groupings, categories, and more. The application offers high accuracy and reliability in categorizing data, with a user-friendly interface and transparent pricing options.

VideoAsk by Typeform is an interactive video platform that helps streamline conversations and build business relationships at scale. It offers features such as asynchronous interviews, easy scheduling, tagging, gathering contact info, capturing leads, research and feedback, training, customer support, and more. Users can connect VideoAsk to over 1,500 applications through Zapier or use the API and webhooks. The platform allows for the creation of video forms, async interviews, and AI-powered video chatbots to engage with the audience 24/7. It is easy to use, code-free, and offers various tools like live calls, video funnels, video chatbots, video forms, asynchronous video interviews, and direct messaging.

Re-VIEW is an AI-powered survey tool that allows users to collect video responses to survey questions, enabling them to see and hear what people think. The tool offers a free forever plan with the option to upgrade if needed. It provides the ability to capture authentic emotional connections through body language and voice intonation, leading to higher response rates and more detailed answers. With automatic insights and AI-driven analysis, users can conduct research at scale efficiently. Re-VIEW aims to simplify the survey process with stunning simplicity and practical pricing, making powerful research accessible to all.

Holler is an AI-powered survey tool that helps businesses collect feedback and gain insights from their customers. It is designed to be simple and easy to use, with a variety of features that make it a powerful tool for businesses of all sizes. Holler can be used to collect feedback on a variety of topics, including customer satisfaction, product development, and employee engagement. The AI-powered insights engine can help businesses identify trends and patterns in the data, and make recommendations for how to improve their products and services.

Survaii is an AI-powered platform revolutionizing the market research industry by delivering accurate, bias-free data for strategic decision-making. The platform empowers startups and enterprises with cutting-edge tools for survey generation, audience targeting, response bias combat, data analysis, and AI-driven reports with actionable insights. Survaii offers a user-friendly interface, scalable solutions, top-notch security, and innovative features like live video call surveys to provide a seamless and insightful market research experience.

Raay is an AI-powered forms and surveys platform that helps users create, share, run, and analyze data. It offers a range of features including highly integrable forms, bias detection, and meaningful insights. Raay's affordable pricing plans make it accessible to users of all levels.

This repository provides a comprehensive survey of research papers on large language model (LLM)-based game agents. LLMs are powerful AI models that can understand and generate human language, and they have shown great promise for developing intelligent game agents. This survey covers a wide range of topics, including adventure games, crafting and exploration games, simulation games, competition games, cooperation games, communication games, and action games. For each topic, the survey provides an overview of the state-of-the-art research, as well as a discussion of the challenges and opportunities for future work.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

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, Awesome-LLM-Survey, serves as a comprehensive collection of surveys related to Large Language Models (LLM). It covers various aspects of LLM, including instruction tuning, human alignment, LLM agents, hallucination, multi-modal capabilities, and more. Researchers are encouraged to contribute by updating information on their papers to benefit the LLM survey community.

Efficient Multimodal Large Language Models: A Survey provides a comprehensive review of efficient and lightweight Multimodal Large Language Models (MLLMs), focusing on model size reduction and cost efficiency for edge computing scenarios. The survey covers the timeline of efficient MLLMs, research on efficient structures and strategies, and applications. It discusses current limitations and future directions in efficient MLLM research.

This repository maintains a list of research papers on LLM-based Multi-Agents, categorized into five main streams: Multi-Agents Framework, Multi-Agents Orchestration and Efficiency, Multi-Agents for Problem Solving, Multi-Agents for World Simulation, and Multi-Agents Datasets and Benchmarks. The repository also includes a survey paper on LLM-based Multi-Agents and a table summarizing the key findings of the survey.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

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.

LLM4IR-Survey is a collection of papers related to large language models for information retrieval, organized according to the survey paper 'Large Language Models for Information Retrieval: A Survey'. It covers various aspects such as query rewriting, retrievers, rerankers, readers, search agents, and more, providing insights into the integration of large language models with information retrieval systems.

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

The repository 'LLM-for-Healthcare' provides a comprehensive survey of large language models (LLMs) for healthcare, covering data, technology, applications, and accountability and ethics. It includes information on various LLM models, training data, evaluation methods, and computation costs. The repository also discusses tasks such as NER, text classification, question answering, dialogue systems, and generation of medical reports from images in the healthcare domain.

A repository sharing the panorama of the methodology literature on Transformer architecture upgrades in Large Language Models for handling extensive context windows, with real-time updating the newest published works. It includes a survey on advancing Transformer architecture in long-context large language models, flash-ReRoPE implementation, latest news on data engineering, lightning attention, Kimi AI assistant, chatglm-6b-128k, gpt-4-turbo-preview, benchmarks like InfiniteBench and LongBench, long-LLMs-evals for evaluating methods for enhancing long-context capabilities, and LLMs-learning for learning technologies and applicated tasks about Large Language Models.

ABigSurveyOfLLMs is a repository that compiles surveys on Large Language Models (LLMs) to provide a comprehensive overview of the field. It includes surveys on various aspects of LLMs such as transformers, alignment, prompt learning, data management, evaluation, societal issues, safety, misinformation, attributes of LLMs, efficient LLMs, learning methods for LLMs, multimodal LLMs, knowledge-based LLMs, extension of LLMs, LLMs applications, and more. The repository aims to help individuals quickly understand the advancements and challenges in the field of LLMs through a collection of recent surveys and research papers.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

Awesome-Colorful-LLM is a meticulously assembled anthology of vibrant multimodal research focusing on advancements propelled by large language models (LLMs) in domains such as Vision, Audio, Agent, Robotics, and Fundamental Sciences like Mathematics. The repository contains curated collections of works, datasets, benchmarks, projects, and tools related to LLMs and multimodal learning. It serves as a comprehensive resource for researchers and practitioners interested in exploring the intersection of language models and various modalities for tasks like image understanding, video pretraining, 3D modeling, document understanding, audio analysis, agent learning, robotic applications, and mathematical research.

This repository, named Awesome-Embodied-Agent-with-LLMs, is a curated list of research related to Embodied AI or agents with Large Language Models. It includes various papers, surveys, and projects focusing on topics such as self-evolving agents, advanced agent applications, LLMs with RL or world models, planning and manipulation, multi-agent learning and coordination, vision and language navigation, detection, 3D grounding, interactive embodied learning, rearrangement, benchmarks, simulators, and more. The repository provides a comprehensive collection of resources for individuals interested in exploring the intersection of embodied agents and large language models.

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

This repository, Awesome-LLM-RAG, aims to record advanced papers on Retrieval Augmented Generation (RAG) in Large Language Models (LLMs). It serves as a resource hub for researchers interested in promoting their work related to LLM RAG by updating paper information through pull requests. The repository covers various topics such as workshops, tutorials, papers, surveys, benchmarks, retrieval-enhanced LLMs, RAG instruction tuning, RAG in-context learning, RAG embeddings, RAG simulators, RAG search, RAG long-text and memory, RAG evaluation, RAG optimization, and RAG applications.

10 simple questions to get a small business owner's opinion on AI and AI tools.

Helps you write market research briefs and survey questions.

What Are They Thinking?

Free to use oracle for analyzing Net Promoter Score data.

Build beautiful forms and surveys for free. Analyze the submission data to uncover insights.

Free, beautiful, and AI-generated product surveys

Expert in marketing research, with a clever twist on its origins.

Teaches and analyzes research methods with in-depth, academic insights.

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Expert in biostatistics, psychometrics, and general statistical analysis.

Expert at crafting engaging questions like Rob Fitzpatrick

ChatPaper作者维护的GPTs

The buddy that helps you with all things Drones. While I'm nicer than most humans, I'm still a bot, so double check my answers

A coach to assist community visioning processes.

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Advises on employee compensation and benefits strategies.

Enhances internal alignment through effective communication strategies.