Best AI tools for< Approximate Human Preference >

GPTKit is a free AI text generation detection tool that utilizes six different AI-based content detection techniques to identify and classify text as either human- or AI-generated. It provides reports on the authenticity and reality of the analyzed content, with an accuracy of approximately 93%. The first 2048 characters in every request are free, and users can register for free to get 2048 characters/request.

ChatTTS is an open-source text-to-speech model designed for dialogue scenarios, supporting both English and Chinese speech generation. Trained on approximately 100,000 hours of Chinese and English data, it delivers speech quality comparable to human dialogue. The tool is particularly suitable for tasks involving large language model assistants and creating dialogue-based audio and video introductions. It provides developers with a powerful and easy-to-use tool based on open-source natural language processing and speech synthesis technologies.

Intuit Assist is a generative AI-powered financial assistant designed to help you achieve financial confidence. It is a comprehensive platform that offers a wide range of financial tools and services, including TurboTax, Credit Karma, QuickBooks, and Mailchimp. Intuit Assist can help you with a variety of financial tasks, such as filing your taxes, managing your credit, tracking your expenses, and invoicing your clients. It is a valuable tool for anyone who wants to take control of their finances and achieve financial success.

ChatTTS is a text-to-speech tool optimized for natural, conversational scenarios. It supports both Chinese and English languages, trained on approximately 100,000 hours of data. With features like multi-language support, large data training, dialog task compatibility, open-source plans, control, security, and ease of use, ChatTTS provides high-quality and natural-sounding voice synthesis. It is designed for conversational tasks, dialogue speech generation, video introductions, educational content synthesis, and more. Users can integrate ChatTTS into their applications using provided API and SDKs for a seamless text-to-speech experience.

Text to Infographic is an AI Infographic Generator that transforms text into visually appealing infographics in approximately 2 minutes. It requires no design skills and is ideal for creating engaging content for blogs, social media, and other platforms. Users can easily convert text on various topics such as dog breeds, superfoods, traveling, and meditation into informative graphics. The service offers a user-friendly experience with affordable pricing and the option to customize and download the generated infographics.

This Beach Does Not Exist is an AI application powered by StyleGAN2-ADA network, capable of generating realistic beach images. The website showcases AI-generated beach landscapes created from a dataset of approximately 20,000 images. Users can explore the training progress of the network, generate random images, utilize K-Means Clustering for image grouping, and download the network for experimentation or retraining purposes. Detailed technical information about the network architecture, dataset, training steps, and metrics is provided. The application is based on the GAN architecture developed by NVIDIA Labs and offers a unique experience of creating virtual beach scenes through AI technology.

HeHealth is an AI-powered application that aims to revolutionize sexual health by providing early detection of sexually transmitted infections (STIs). The application collaborates with public health entities to transform sexual health care delivery at a population level. With over 400 million people contracting STIs globally every year, HeHealth addresses the critical gap of undiagnosed cases by offering a discreet and efficient AI-powered Penis Health Checker. The tool provides instant results in the comfort and privacy of one's home, with a detection accuracy of approximately 94% for visual symptoms, at a fraction of the cost of traditional methods.

Image In Words is a generative model designed for scenarios that require generating ultra-detailed text from images. It leverages cutting-edge image recognition technology to provide high-quality and natural image descriptions. The framework ensures detailed and accurate descriptions, improves model performance, reduces fictional content, enhances visual-language reasoning capabilities, and has wide applications across various fields. Image In Words supports English and has been trained using approximately 100,000 hours of English data. It has demonstrated high quality and naturalness in various tests.

Twitter Personality - AI Agent by Wordware is an AI application that analyzes your tweets to unveil your unique personality traits. It provides insights on your love life, goals, and how others perceive you. Powered by Wordware, an IDE for building AI agents, users can create their own AI app in approximately 15 minutes. The application is designed to help users discover more about themselves through their Twitter activity.

Arena-Hard-Auto-v0.1 is an automatic evaluation tool for instruction-tuned LLMs. It contains 500 challenging user queries. The tool prompts GPT-4-Turbo as a judge to compare models' responses against a baseline model (default: GPT-4-0314). Arena-Hard-Auto employs an automatic judge as a cheaper and faster approximator to human preference. It has the highest correlation and separability to Chatbot Arena among popular open-ended LLM benchmarks. Users can evaluate their models' performance on Chatbot Arena by using Arena-Hard-Auto.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

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.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

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 tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

This repository is a collection of papers on Generative AI Machine Unlearning, categorized based on modality and applications. It includes datasets, benchmarks, and surveys related to unlearning scenarios in generative AI. The repository aims to provide a comprehensive overview of research in the field of machine unlearning for generative models.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

LLM for Unity enables seamless integration of Large Language Models (LLMs) within the Unity engine, allowing users to create intelligent characters for immersive player interactions. The tool supports major LLM models, runs locally without internet access, offers fast inference on CPU and GPU, and is easy to set up with a single line of code. It is free for both personal and commercial use, tested on Unity 2021 LTS, 2022 LTS, and 2023. Users can build multiple AI characters efficiently, use remote servers for processing, and customize model settings for text generation.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

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