Best AI tools for< find research findings >

arXiv.org is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

PaperClip is a tool that helps you keep track of your daily AI papers review. You can use it to memorize details from papers in machine learning, computer vision, and natural language processing. PaperClip's AI runs locally and doesn’t send data to any server. This means that your data is safe and secure. PaperClip has a number of features that make it a great tool for AI researchers. These features include: * The ability to find back important findings quickly and easily. * The ability to memorize from anywhere, including AI research papers, ML blog posts, and news articles. * The ability to get your findings back with a simple search. * The ability to clean your data anytime. * The ability to delete all data with one click.

Cactus Communications is a science communication and technology company specializing in AI products and solutions for research funding, publication, communication, and discovery. Their services include editorial services, author education, research promotion, technology solutions, and medical communications.

AcademicID is an AI-powered platform that helps students and researchers discover and access academic resources. It provides a comprehensive database of academic papers, journals, and other resources, as well as tools to help users organize and manage their research. AcademicID also offers a variety of features to help users collaborate with others and share their research findings.

Seamless is an AI-powered literature review tool that helps researchers draft literature reviews 100x faster. It searches the Semantic Scholar database of scientific papers and blends together the relevant papers with the user description to create a literature review. The last step is done using large language models like GPT-4.

Summarize Paper .com is an open-source AI tool that provides concise, understandable, and insightful summaries of the latest research articles on arXiv. The tool uses AI to generate key points and layman's summaries of research papers, making it easy for users to stay up-to-date with the latest developments in their field. In addition to its summary service, Summarize Paper .com also offers an AI assistant that can answer questions about arXiv papers. The tool is designed to make it easy for researchers, students, journalists, and anyone else who wants to stay informed about the latest research to access and understand the latest findings.

Lateral is an AI-powered research tool that helps academics and students streamline their workflow by seamlessly searching, saving, and organizing findings across their papers. It uses AI to generate an auto-generated table, name concepts, and provide super search capabilities, making it easy to find relevant information quickly. Lateral also allows users to collaborate and share their work, making it a valuable tool for researchers working on collaborative projects.

The Google Research Blog is a platform for researchers at Google to share their latest work in artificial intelligence, machine learning, and other related fields. The blog covers a wide range of topics, from theoretical research to practical applications. The goal of the blog is to provide a forum for researchers to share their ideas and findings, and to foster collaboration between researchers at Google and around the world.

Epsilon is an AI-powered search engine that helps researchers find answers to their questions using academic literature. It uses AI to summarize relevant passages from over 200 million papers and provides inline references to the source content. Epsilon can also help researchers find publications and patents, validate claims in the literature, and save and summarize papers across libraries.

Elicit is an AI research assistant that helps researchers analyze research papers at superhuman speed. It automates time-consuming research tasks such as summarizing papers, extracting data, and synthesizing findings. Trusted by researchers, Elicit offers a plethora of features to speed up the research process and is particularly beneficial for empirical domains like biomedicine and machine learning.

ResearchRabbit is a research tool that helps researchers discover and organize academic papers. It uses artificial intelligence to recommend papers that are relevant to a researcher's interests and to visualize networks of papers and co-authorships. ResearchRabbit also allows researchers to collaborate on collections of papers and to share their findings with others.

Lime is an AI-powered data research assistant that helps you find and analyze data faster and more efficiently. With Lime, you can: * **Search and discover data** from a variety of sources, including public datasets, private databases, and the web. * **Clean and prepare data** for analysis, using a variety of tools and techniques. * **Analyze data** using a variety of statistical and machine learning techniques. * **Visualize data** in a variety of ways, including charts, graphs, and maps. * **Share your findings** with others, using a variety of export options.

wisio is an AI-powered writing assistant that helps scientists write better scientific documents. It provides real-time feedback on grammar, style, and clarity, and it can also help you to generate new ideas and organize your thoughts. wisio is designed to make scientific writing faster, easier, and more effective.

Humata is a PDF AI that can summarize findings, compare documents, and search for answers in long technical papers. It is designed to help users save time and effort by automating the process of reading and understanding complex documents. Humata is easy to use and can be embedded in any webpage with a single click. It is also secure and reliable, with enterprise-grade data rooms and encryption to protect user data.

WhyHive is an AI-powered data analysis tool that helps users find and code key themes in their data, then visualize their findings with beautiful, shareable charts. It is designed to be easy to use, even for those with no prior experience with data analysis. WhyHive can analyze thousands of rows of data in minutes, saving users hours of manual coding time. It can also be used to analyze both quantitative and qualitative data, making it a versatile tool for a variety of research projects.

Find AI is an AI-powered search engine that provides users with advanced search capabilities to unlock contact details and gain more accurate insights. The platform caters to individuals and companies looking to research people, companies, startups, founders, and more. Users can access email addresses and premium search features to explore a wide range of data related to various industries and sectors. Find AI offers a user-friendly interface and efficient search algorithms to deliver relevant results in a timely manner.

SmartScout is an Amazon FBA product research software designed to help businesses grow their wholesale, arbitrage, and private label operations. It provides users with access to millions of data points, including product sales data, seller information, and competitive insights. SmartScout's features include a product database, brand and category filters, a traffic graph, and a variety of tools for product research and analysis.

Sourcely is an AI-powered source-finding tool that helps students and researchers find credible academic sources quickly and easily. Simply paste your essay or paragraph into Sourcely, and it will generate a list of relevant sources. You can then filter the results by publication year, authorship, relevance, and more. Sourcely also provides summaries of each source, making it easy to get an overview of the material. With Sourcely, you can save hours of manual research and ensure that your sources are credible.

AnswerTime is an AI-led research tool that leverages artificial intelligence to provide quick and accurate answers to a wide range of research questions. The platform is designed to assist users in finding relevant information efficiently, saving time and effort in the research process. AnswerTime utilizes advanced algorithms to analyze and process data from various sources, delivering reliable results in a matter of seconds. With its user-friendly interface and powerful AI capabilities, AnswerTime is a valuable tool for students, professionals, and researchers seeking to enhance their research productivity.

Rankify is an AI SEO keyword research tool designed for SEO teams, freelancers, and agencies. It simplifies the process of finding relevant keywords and blog topics by allowing users to input seed keywords or semantically describe the keywords they want to find. The tool offers features such as search volume analysis, color-coded keyword difficulty, keyword lists segmentation, bulk copy and paste, and the ability to manage multiple projects. Rankify also provides enterprise-grade encryption and security for data protection.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

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 presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.

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.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

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.

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.

AI-Writer is an AI content generation toolkit called Alwrity that automates and enhances the process of blog creation, optimization, and management. It integrates advanced AI models for text generation, image creation, and data analysis, offering features such as online research integration, long-form content generation, AI content planning, multilingual support, prevention of AI hallucinations, multimodal content generation, SEO optimization, and integration with platforms like Wordpress and Jekyll. The toolkit is designed for automated blog management and requires appropriate API keys and access credentials for full functionality.

This repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more. It includes monthly best GenAI papers list, interview resources, free courses, and code repositories/notebooks for developing generative AI applications. The repository is regularly updated with the latest additions to keep users informed and engaged in the field of generative AI.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

This repository is a curated list of papers focused on the detection of Large Language Models (LLMs)-generated content. It includes the latest research papers covering detection methods, datasets, attacks, and more. The repository is regularly updated to include the most recent papers in the field.

This repository is a collection of papers focused on understanding the internal mechanism of large language models (LLM). It includes research on topics such as how LLMs handle multilingualism, learn in-context, and handle factual associations. The repository aims to provide insights into the inner workings of transformer-based language models through a curated list of papers and surveys.

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.

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.

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.

Welcome to our Awesome-llm-safety repository! We've curated a collection of the latest, most comprehensive, and most valuable resources on large language model safety (llm-safety). But we don't stop there; included are also relevant talks, tutorials, conferences, news, and articles. Our repository is constantly updated to ensure you have the most current information at your fingertips.

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.

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