Best AI tools for< Disclose Content Origin >

Aithenticate is a platform that focuses on transparency in Artificial Intelligence (AI) usage. It helps users achieve greater compliance with AI regulations and ensures transparency by disclosing whether content is generated by humans or AI. The platform offers a WordPress plug-in for easy integration, a company profile page with detailed AI disclosure, an AI disclosure generator, and clear icons to signify AI or human-generated content. Aithenticate aims to enhance peace of mind, trust, and clarity for users interacting with AI-generated content.

Waitlyst.co is a website that is currently under development and not yet launched. The domain is registered at Dynadot.com and is expected to be live in the future. The purpose and functionality of the website are not disclosed in the provided text, but it seems to be related to managing waitlists or reservations for events, services, or products. The website is expected to offer a platform for users to create and manage waitlists efficiently.

FeedGen is an open-source tool that uses Google Cloud's state-of-the-art Large Language Models (LLMs) to improve product titles, generate more comprehensive descriptions, and fill missing attributes in product feeds. It helps merchants and advertisers surface and fix quality issues in their feeds using Generative AI in a simple and configurable way. The tool relies on GCP's Vertex AI API to provide both zero-shot and few-shot inference capabilities on GCP's foundational LLMs. With few-shot prompting, users can customize the model's responses towards their own data, achieving higher quality and more consistent output. FeedGen is an Apps Script based application that runs as an HTML sidebar in Google Sheets, allowing users to optimize their feeds with ease.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

wdoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It aims to handle large volumes of diverse document types, making it ideal for researchers, students, and professionals dealing with extensive information sources. wdoc uses LangChain to process and analyze documents, supporting tens of thousands of documents simultaneously. The system includes features like high recall and specificity, support for various Language Model Models (LLMs), advanced RAG capabilities, advanced document summaries, and support for multiple tasks. It offers markdown-formatted answers and summaries, customizable embeddings, extensive documentation, scriptability, and runtime type checking. wdoc is suitable for power users seeking document querying capabilities and AI-powered document summaries.

This competition is organized by Javier Rando and Florian Tramèr from the ETH AI Center and SPY Lab at ETH Zurich. The goal of the competition is to create a method that can detect universal backdoors in aligned language models. A universal backdoor is a secret suffix that, when appended to any prompt, enables the model to answer harmful instructions. The competition provides a set of poisoned generation models, a reward model that measures how safe a completion is, and a dataset with prompts to run experiments. Participants are encouraged to use novel methods for red-teaming, automated approaches with low human oversight, and interpretability tools to find the trojans. The best submissions will be offered the chance to present their work at an event during the SaTML 2024 conference and may be invited to co-author a publication summarizing the competition results.

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.

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.

WARC-GPT is an experimental retrieval augmented generation pipeline for web archive collections. It allows users to interact with WARC files, extract text, generate text embeddings, visualize embeddings, and interact with a web UI and API. The tool is highly customizable, supporting various LLMs, providers, and embedding models. Users can configure the application using environment variables, ingest WARC files, start the server, and interact with the web UI and API to search for content and generate text completions. WARC-GPT is designed for exploration and experimentation in exploring web archives using AI.

LangChain for Swift. Optimized for iOS, macOS, watchOS (part) and visionOS.(beta) This is a pure client library, no server required

Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images. It seamlessly handles forms, invoices, receipts, and other unstructured data sources. Sparrow stands out with its modular architecture, offering independent services and pipelines all optimized for robust performance. One of the critical functionalities of Sparrow - pluggable architecture. You can easily integrate and run data extraction pipelines using tools and frameworks like LlamaIndex, Haystack, or Unstructured. Sparrow enables local LLM data extraction pipelines through Ollama or Apple MLX. With Sparrow solution you get API, which helps to process and transform your data into structured output, ready to be integrated with custom workflows. Sparrow Agents - with Sparrow you can build independent LLM agents, and use API to invoke them from your system. **List of available agents:** * **llamaindex** - RAG pipeline with LlamaIndex for PDF processing * **vllamaindex** - RAG pipeline with LLamaIndex multimodal for image processing * **vprocessor** - RAG pipeline with OCR and LlamaIndex for image processing * **haystack** - RAG pipeline with Haystack for PDF processing * **fcall** - Function call pipeline * **unstructured-light** - RAG pipeline with Unstructured and LangChain, supports PDF and image processing * **unstructured** - RAG pipeline with Weaviate vector DB query, Unstructured and LangChain, supports PDF and image processing * **instructor** - RAG pipeline with Unstructured and Instructor libraries, supports PDF and image processing. Works great for JSON response generation

This is the official repository for the ACM CCS 2024 paper 'Do Anything Now': Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. The project employs a new framework called JailbreakHub to conduct the first measurement study on jailbreak prompts in the wild, collecting 15,140 prompts from December 2022 to December 2023, including 1,405 jailbreak prompts. The dataset serves as the largest collection of in-the-wild jailbreak prompts. The repository contains examples of harmful language and is intended for research purposes only.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

The Korean SAT LLM Leaderboard is a benchmarking project that allows users to test their fine-tuned Korean language models on a 10-year dataset of the Korean College Scholastic Ability Test (CSAT). The project provides a platform to compare human academic ability with the performance of large language models (LLMs) on various question types to assess reading comprehension, critical thinking, and sentence interpretation skills. It aims to share benchmark data, utilize a reliable evaluation dataset curated by the Korea Institute for Curriculum and Evaluation, provide annual updates to prevent data leakage, and promote open-source LLM advancement for achieving top-tier performance on the Korean CSAT.

CodeLLM Paper repository provides a curated list of research papers focused on Large Language Models (LLMs) for code. It aims to facilitate researchers and practitioners in exploring the rapidly growing body of literature on this topic. The papers are systematically collected from various top-tier venues, categorized, and labeled for easier navigation. The selection strategy involves abstract extraction, keyword matching, relevance check using LLMs, and manual labeling. The papers are categorized based on Application, Principle, and Research Paradigm dimensions. Contributions to expand the repository are welcome through PR submission, issue submission, or request for batch updates. The repository is intended solely for research purposes, with raw data sourced from publicly available information on ACM, IEEE, and corresponding conference websites.

Nanobrowser is an open-source AI web automation tool that runs in your browser. It is a free alternative to OpenAI Operator with flexible LLM options and a multi-agent system. Nanobrowser offers premium web automation capabilities while keeping users in complete control, with features like a multi-agent system, interactive side panel, task automation, follow-up questions, and multiple LLM support. Users can easily download and install Nanobrowser as a Chrome extension, configure agent models, and accomplish tasks such as news summary, GitHub research, and shopping research with just a sentence. The tool uses a specialized multi-agent system powered by large language models to understand and execute complex web tasks. Nanobrowser is actively developed with plans to expand LLM support, implement security measures, optimize memory usage, enable session replay, and develop specialized agents for domain-specific tasks. Contributions from the community are welcome to improve Nanobrowser and build the future of web automation.

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.