Best AI tools for< Improve Feedback Collection >

Wizu is an AI-powered conversational survey platform that revolutionizes customer feedback collection. By leveraging artificial intelligence, Wizu offers insightful and actionable feedback through intelligent follow-up questions, enhanced engagement, and quality insights. The platform combines AI efficiency with human-centric interactions to provide personalized feedback at scale. With features like AI-driven surveys, intelligent follow-up questions, and data stories, Wizu helps businesses make informed decisions and improve customer experience management.

AI Top Rank is an AI tool designed to gather feedback and feature requests from users. It provides a platform for users to share their ideas, suggestions, and feedback on various topics. The tool helps in organizing and prioritizing feedback to improve products or services. With AI capabilities, it offers intelligent insights and analysis to drive decision-making processes. AI Top Rank aims to enhance user engagement and satisfaction by incorporating user feedback into product development.

LoomFlows is a user feedback collection tool that allows businesses to collect high-quality feedback from their users through Loom screen recordings and annotated screenshots. It helps businesses streamline feedback collection, identify impactful opportunities, and scale faster by building the right features.

RhetorAI is a feedback collection AI for businesses. It uses ChatGPT to interview customers and provide insights. With RhetorAI, businesses can get real feedback from their users anytime, anywhere. It's like having a 24/7 product researcher on your team.

Feedbuck AI is an AI-powered user feedback collection tool that helps businesses collect feedback from their users quickly and easily. It uses AI to automatically generate feedback questions based on your website and objectives, and it provides straightforward summaries with clear results. Feedbuck AI is easy to use and integrates with a variety of platforms, making it a great choice for businesses of all sizes.

MagicLoop is a voice survey tool designed to enhance customer feedback by replacing written feedback with spoken responses. It allows users to gather higher-quality responses through voice surveys, capturing emotions, tones, and nuances for a deeper understanding of participants' feelings and intentions. The tool aims to improve participant engagement and provide detailed insights by encouraging genuine responses. MagicLoop offers a modern approach to surveys, addressing the limitations of traditional methods and providing tailored solutions for various use cases such as user research, satisfaction surveys, NPS, feedback collection, market research, and data monitoring. With features like AI analysis, speech-to-text transcription, and custom branding, MagicLoop streamlines the process of generating insights from voice recordings.

Paymefy is an AI-powered debt collection optimization tool that helps businesses recover outstanding payments faster and at a lower cost. By leveraging Artificial Intelligence, Paymefy automates the debt collection process, offers on-click payment solutions, and provides smart notification sequences to enhance customer communication. The tool enables easy customization, multi-invoice payments, installment options, and feedback collection to streamline the debt recovery process. With a focus on efficiency and cost reduction, Paymefy aims to improve debt collection outcomes and customer payment experiences.

Craftman is an AI chatbot builder that allows users to create custom ChatGPT chatbots for sales and support. The platform enables users to train ChatGPT with their own data and easily add the AI bots chat widget to their website for faster and more efficient customer support. Craftman offers features such as instant responses to visitor questions, effortless feedback collection, direct feature request channel, and personalized user engagement. The application provides advantages like 24/7 availability, instant responses, cost-efficiency, personalization, and enhanced user engagement. However, some disadvantages include the need for internet connectivity, potential language limitations, and initial setup time. Craftman is designed to streamline customer interactions, boost sales, and improve user satisfaction through AI-driven chatbot technology.

AskMore is an AI-powered platform for conducting user interviews and product research. The tool utilizes artificial intelligence to streamline the interview process, enabling users to gather feedback more efficiently and in multiple languages. By automating tasks such as generating reports and translating interviews, AskMore helps users access valuable insights and reach a broader audience. The platform aims to enhance the user research experience by offering a user-friendly interface and research best practices to ensure high-quality feedback.

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.

Trove is an AI-powered platform that enables users to create ChatGPT-like forms and surveys. It leverages advanced natural language processing technology to streamline the process of gathering information and feedback from users. With Trove, users can easily design interactive and engaging forms and surveys to collect valuable insights and data. The platform offers a user-friendly interface and customizable features to cater to various needs and preferences. Trove is designed to enhance user engagement and improve data collection efficiency for businesses, researchers, educators, and other professionals.

InteliConvo® is a state-of-the-art AI-powered speech analytics and automation platform that enables businesses to process and analyze 100% of recorded customer conversations. It provides valuable insights into customer buying patterns, intents, sentiments, and feedback, which can be utilized to automate workflows, accelerate sales, improve debt collections, boost customer experience, and ensure compliance. The platform offers features like multilingual support, flexible deployment options, hot lead identification, debt default prediction, brand building insights, and compliance monitoring.

Umbi Space is an AI-powered website builder and online ordering system specifically designed for restaurants. It offers a range of features such as restaurant website templates, delivery and takeaway ordering, dine-in order and pay functionality, contactless menu options, and a review collection system. With Umbi Space, restaurants can easily create a professional online presence, increase profits, enhance customer experience, and streamline operations. The platform provides industry-specific templates that can be customized to match each restaurant's unique style and needs.

Zonka Feedback is a powerful Customer Feedback and Survey Platform that offers User Segmentation for precise targeting, AI capabilities for smarter surveys, and a wide range of features to measure and improve Customer Experience. It provides solutions for various industries and use cases, integrates with popular tools, and offers in-depth reporting and analytics. Zonka Feedback is known for its modern-looking surveys, ease of use, and extensive integrations, making it a versatile tool for collecting feedback from customers, users, visitors, patients, and employees.

Rargus is a generative AI tool that specializes in turning customer feedback into actionable insights for businesses. By collecting feedback from various channels and utilizing custom AI analysis, Rargus helps businesses understand customer needs and improve product development. The tool enables users to compile and analyze feedback efficiently, leading to data-driven decision-making and successful product launches. Rargus also offers solutions for consumer insights, product management, and product marketing, helping businesses enhance customer satisfaction and drive growth.

Sprig is an all-in-one product experience platform that leverages AI technology to provide actionable insights and recommendations for optimizing user experiences. It offers features such as Replays for capturing user behavior, Heatmaps for visualizing interactions, Surveys for collecting feedback, AI Explorer for holistic AI insights, and AI Recommendations for generating product solutions. Sprig helps product managers, user researchers, customer experience teams, and engineers to continuously improve their products by understanding user behavior, identifying pain points, and enhancing conversion rates.

CourseFactory AI is an online platform that leverages artificial intelligence to assist users in creating high-quality online courses efficiently. The platform offers a range of AI assistants to help with course creation, from generating ideas and organizing content to collecting feedback and continuously improving the course structure. CourseFactory AI aims to streamline the course creation process, save time, and enhance the learning experience for both creators and students.

ProProfs is a user research and customer feedback software that offers a comprehensive suite of tools for collecting real-time insights from website visitors and app users. With features like AI sentiment analysis, question branching, branding customization, advanced user targeting, and nudge for prototype, ProProfs empowers businesses to gather valuable feedback to enhance user experience, product functionality, and customer satisfaction. The application is designed to help businesses improve website leads, iOS & Android app feedback, NPS, customer experience management, ecommerce conversion, SaaS conversions, and marketing performance. ProProfs is known for its AI-powered analytics and reports, user-friendly interface, and exceptional customer support.

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.

ListenUp! is an AI-powered discovery tool designed for busy product teams to streamline the process of collecting and analyzing user feedback. The application automatically centralizes user feedback, orders it, and scales the process with AI technology. It helps product teams understand their users better, make informed decisions, and deliver more value efficiently. ListenUp! offers features such as automated feedback capture, real-time pattern suggestions, and transcribing user interviews with multiple speakers. The tool aims to enhance user understanding, improve product development, and boost team performance.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

This repository contains a collection of sample applications and Jupyter Notebooks for hands-on experience with Pinecone vector databases and common AI patterns, tools, and algorithms. It includes production-ready examples for review and support, as well as learning-optimized examples for exploring AI techniques and building applications. Users can contribute, provide feedback, and collaborate to improve the resource.

Awesome-Story-Generation is a repository that curates a comprehensive list of papers related to Story Generation and Storytelling, focusing on the era of Large Language Models (LLMs). The repository includes papers on various topics such as Literature Review, Large Language Model, Plot Development, Better Storytelling, Story Character, Writing Style, Story Planning, Controllable Story, Reasonable Story, and Benchmark. It aims to provide a chronological collection of influential papers in the field, with a focus on citation counts for LLMs-era papers and some earlier influential papers. The repository also encourages contributions and feedback from the community to improve the collection.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

This repository provides a comprehensive collection of AI tools and resources to enhance literature reviews. It includes a curated list of AI tools for various tasks, such as identifying research gaps, discovering relevant papers, visualizing paper content, and summarizing text. Additionally, the repository offers materials on generative AI, effective prompts, copywriting, image creation, and showcases of AI capabilities. By leveraging these tools and resources, researchers can streamline their literature review process, gain deeper insights from scholarly literature, and improve the quality of their research outputs.

AIMET is a library that provides advanced model quantization and compression techniques for trained neural network models. It provides features that have been proven to improve run-time performance of deep learning neural network models with lower compute and memory requirements and minimal impact to task accuracy. AIMET is designed to work with PyTorch, TensorFlow and ONNX models. We also host the AIMET Model Zoo - a collection of popular neural network models optimized for 8-bit inference. We also provide recipes for users to quantize floating point models using AIMET.

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.

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **Try out our [live research preview](https://storm.genie.stanford.edu/) to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!**

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It leverages Large Language Models (LLMs) to interact with spreadsheets like a human expert, enabling non-expert users to complete tasks on complex software such as Google Sheets and Excel via a language interface. The tool observes spreadsheet states, polishes generated solutions based on external action documents and error feedback, and aims to improve success rate and efficiency. SheetCopilot offers a dataset with diverse task categories and operations, supporting operations like entry & manipulation, management, formatting, charts, and pivot tables. Users can interact with SheetCopilot in Excel or Google Sheets, executing tasks like calculating revenue, creating pivot tables, and plotting charts. The tool's evaluation includes performance comparisons with leading LLMs and VBA-based methods on specific datasets, showcasing its capabilities in controlling various aspects of a spreadsheet.

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.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

Rubra is a collection of open-weight large language models enhanced with tool-calling capability. It allows users to call user-defined external tools in a deterministic manner while reasoning and chatting, making it ideal for agentic use cases. The models are further post-trained to teach instruct-tuned models new skills and mitigate catastrophic forgetting. Rubra extends popular inferencing projects for easy use, enabling users to run the models easily.

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

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.

An English teacher that offers concise corrections and engages in conversation.

English language expert aiding non-English speakers with text corrections.

Du schreibst, ich korrigiere.

An AI-Powered Tool for Detecting and Correcting Errors in Homework Problems

I'm eager to read your work and give you feedback to improve your skills.

Flutter UI code generator with a focus on responsive, beautiful, scalable UI. Share feedback to improve @5hirish on X

Helps improve language skills through interactive scenarios and feedback.

Upload a screenshot of your Tinder profile to improve it and get feedback

Facilitates customer satisfaction through effective feedback management.

An assistant that intends to help TAs respond to their students' emails, messages, provide homework feedback and give instructions, and help improve the section discussion slides.

Analyses and gives actionable feedback on web Review type content using Google's Reviews System guidelines and Google's Quality Rater Guidelines

The UX Feedback GPT specializes in critiquing UX/UI design, focusing on accessibility, layout, and best practices from Nielsen Norman Group and IDEO. It offers tailored feedback for various design stages and emphasizes clear communication, responsiveness, and ethical design principles.

Feedback on your consultancy proposals.

Advanced tool for crafting impactful employee reviews.

An ultra-honest, harsh writing critic offering hyper-constructive feedback.

Fun 11+ exam tutor with interactive feedback and a scoring monster.

Analyzes your writing style and offers personalized feedback

A mentor who provides detailed feedback on student work.

I provide specific UX or Product Design feedback.

Give me a lesson and I can give you feedback based on Rosenshine's "Principles of Instruction"