Best AI tools for< Understand Research Findings >

The website focuses on groundbreaking research and studies across various scientific fields, including quantum communication, semiconductor dynamics, mental health support, dietary interventions, computer vision, autonomous systems safety, machine learning applications, and astrophysical observations. It covers innovative findings, theoretical studies, and technological advancements that have the potential to reshape industries and scientific understanding.

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.

Trill is an AI-powered research assistant designed to streamline the user research process. It helps users move from interviews to insights quickly by providing relevant insights and observations based on project objectives. With features like instant themes and categories, organizing findings, and a user-friendly editor, Trill aims to simplify and accelerate the research analysis process. Currently in beta, Trill offers a free trial for users to experience its capabilities and provide feedback for further improvements.

Dokkio is an AI-powered platform that helps users find, organize, and understand all of their online files. By utilizing AI technology, Dokkio enables users to work with their cloud files efficiently and collaboratively. The platform offers tools for managing multiple activities, finding documents and files, compiling research materials, and organizing a content library. Dokkio aims to streamline the process of accessing and utilizing online content, making it easier for users to stay organized and productive.

WhiteBridge is an AI-powered online reputation management tool that helps individuals and businesses transform scattered online data into a coherent narrative of their digital identity. By finding, verifying, and structuring information about someone into insightful reports, WhiteBridge enables users to safeguard their reputation, understand prospects, prepare for pitches, hire wisely, and verify authenticity. The tool offers real-time validation, background analysis, and access to over 100 public data APIs to provide unmatched quality of information. WhiteBridge is designed for recruiters, sales reps, business owners, and privacy-conscious individuals to streamline background checks, build better connections, verify information, and safeguard personal data.

The One-Stop Natural Language Hotel Recommender is an AI-powered tool that simplifies the process of finding the perfect hotel for your needs. By utilizing natural language processing technology, the tool can understand your preferences and requirements, and provide you with personalized hotel recommendations. It considers factors such as proximity to popular places, top-rated establishments, summarized reviews, and staying within your budget. With this tool, you can easily find the ideal accommodation for your next trip without the hassle of extensive research.

Explainpaper is an AI-powered tool designed to simplify and explain complex research papers. Users can upload a paper, highlight confusing text, and receive explanations to make the content easier to understand. The tool leverages AI and machine learning models to break down dense sections and clarify intricate concepts, ultimately making research papers more accessible to a wider audience. It is a valuable resource for researchers, students, and anyone looking to delve into complex topics with confidence.

Bard PDF is an AI-powered tool that allows users to interact with PDF documents through natural language conversation. It can summarize documents, answer questions, and extract key information. Bard PDF is designed to help researchers, students, and professionals save time and improve their productivity.

Scite is an award-winning platform for discovering and evaluating scientific articles via Smart Citations. Smart Citations allow users to see how a publication has been cited by providing the context of the citation and a classification describing whether it provides supporting or contrasting evidence for the cited claim.

PaperGuide.AI is an AI-powered research platform that helps users discover, read, write, and manage research with ease. It offers features such as AI search to discover new papers, summaries to understand complex research, reference management, note-taking, and AI writing assistance. Trusted by over 500,000 users, PaperGuide.AI streamlines academic and research workflows by providing tools to synthesize research faster, manage references effectively, and write essays and research papers efficiently.

Jotlify is an AI-powered platform that simplifies complex research papers, making them accessible and easy to understand for students, researchers, professionals, and curious minds. It transforms dense academic content into engaging stories and insights, bridging the gap between complex research and easy understanding. With Jotlify, users can uncover stories and insights that can transform their understanding and impact various aspects of their lives.

Papertalk.io is an AI-powered platform that revolutionizes research by providing users with access to over 215 million papers, AI-generated explanations, and actionable insights. The platform offers precision search tools, AI-powered understanding of research papers, and personalized guidance on applying insights practically. Papertalk.io aims to make research more accessible and approachable for users from diverse backgrounds, transforming complex data into easy-to-digest formats to foster innovation and expertise.

SciSpace is an AI-powered tool that helps researchers understand research papers better. It can explain and elaborate most academic texts in simple words. It is a great tool for students, researchers, and anyone who wants to learn more about a particular topic. SciSpace has a user-friendly interface and is easy to use. Simply upload a research paper or enter a URL, and SciSpace will do the rest. It will highlight key concepts, provide definitions, and generate a summary of the paper. SciSpace can also be used to generate citations and find related papers.

Dr.Oracle is a personal AI research assistant that helps you find and understand the latest research in your field. With Dr.Oracle, you can search for research papers, track your favorite authors, and get personalized recommendations for new research. Dr.Oracle is the perfect tool for students, researchers, and anyone who wants to stay up-to-date on the latest research in their field.

EmpathixAI is an innovative AI tool designed to analyze and interpret human emotions through text and voice inputs. The tool uses advanced natural language processing and sentiment analysis algorithms to provide accurate insights into the emotional state of individuals. EmpathixAI helps businesses understand customer feedback, improve communication strategies, and enhance user experiences. With its user-friendly interface and powerful analytics capabilities, EmpathixAI is a valuable tool for companies looking to gain a deeper understanding of customer sentiment and emotions.

Paperguide is an AI Research Platform that offers an all-in-one solution for researchers and students to discover, read, write, manage research papers with ease. It provides AI-powered Reference Manager and Writing Assistant to help users understand papers, manage references, annotate/take notes, and supercharge their writing process. With features like AI Search, Instant Summaries, Effortless Annotations, and Flawless Citations, Paperguide aims to streamline the academic and research workflow for its users.

Semantic Scholar is a free, AI-powered research tool for scientific literature. It is based at the Allen Institute for AI and provides access to over 217 million papers from all fields of science. Semantic Scholar uses AI to help users discover and explore scientific literature, and to stay up-to-date on the latest research. The tool also includes a number of features to help users manage their research, such as the ability to save papers, create bibliographies, and share research with others.

SticAI Glance is an AI-powered tool that summarizes Reddit posts into actionable insights within seconds. Users can simply paste the link and receive quick summaries. The tool is designed to provide users with a convenient way to extract key information from Reddit posts efficiently. SticAI Glance is developed by ThisUX Private Limited, aiming to streamline information consumption for Reddit users.

Linfo.ai is an AI-powered Article & Youtube Summary & Mind Map tool with GPT Extension that provides users with instant summaries and structured insights from articles, reports, and videos. It allows users to dive deep into any topic, surface valuable insights effortlessly, and customize content hierarchy for quick navigation and comprehension. The tool is designed for professionals who need to process information quickly and efficiently.

The Research Center Trustworthy Data Science and Security is a hub for interdisciplinary research focusing on building trust in artificial intelligence, machine learning, and cyber security. The center aims to develop trustworthy intelligent systems through research in trustworthy data analytics, explainable machine learning, and privacy-aware algorithms. By addressing the intersection of technological progress and social acceptance, the center seeks to enable private citizens to understand and trust technology in safety-critical applications.

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.

CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.

LLM-for-misinformation-research is a curated paper list of misinformation research using large language models (LLMs). The repository covers methods for detection and verification, tools for fact-checking complex claims, decision-making and explanation, claim matching, post-hoc explanation generation, and other tasks related to combating misinformation. It includes papers on fake news detection, rumor detection, fact verification, and more, showcasing the application of LLMs in various aspects of misinformation research.

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.

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.

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

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-LLM-in-Social-Science is a repository that compiles papers evaluating Large Language Models (LLMs) from a social science perspective. It includes papers on evaluating, aligning, and simulating LLMs, as well as enhancing tools in social science research. The repository categorizes papers based on their focus on attitudes, opinions, values, personality, morality, and more. It aims to contribute to discussions on the potential and challenges of using LLMs in social science research.

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.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

This repository is a curated list of research papers that explore the integration of Large Language Model (LLM) technology with tabular data. It aims to provide a comprehensive resource for researchers and practitioners interested in this emerging field. The repository includes papers on a wide range of topics, including table-to-text generation, table question answering, and tabular data classification. It also includes a section on related datasets and resources.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

LLM-PLSE-paper is a repository focused on the applications of Large Language Models (LLMs) in Programming Language and Software Engineering (PL/SE) domains. It covers a wide range of topics including bug detection, specification inference and verification, code generation, fuzzing and testing, code model and reasoning, code understanding, IDE technologies, prompting for reasoning tasks, and agent/tool usage and planning. The repository provides a comprehensive collection of research papers, benchmarks, empirical studies, and frameworks related to the capabilities of LLMs in various PL/SE tasks.

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 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.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

InstructGraph is a framework designed to enhance large language models (LLMs) for graph-centric tasks by utilizing graph instruction tuning and preference alignment. The tool collects and decomposes 29 standard graph datasets into four groups, enabling LLMs to better understand and generate graph data. It introduces a structured format verbalizer to transform graph data into a code-like format, facilitating code understanding and generation. Additionally, it addresses hallucination problems in graph reasoning and generation through direct preference optimization (DPO). The tool aims to bridge the gap between textual LLMs and graph data, offering a comprehensive solution for graph-related tasks.

I assist with SPSS data interpretation for academic manuscripts.

Upload an image of a statistical analysis and we'll interpret the results: linear regression, logistic regression, ANOVA, cluster analysis, MDS, factor analysis, and many more

I will help you finding and understanding scientific studies

A GPT that explains research methods in a language that everyone can easily understand.

Join millions of students, researchers and professionals to instantly answer questions and understand research with Al

論文のPDFをアップロードすると、内容を日本語で分かりやすく説明します（OpenAI側の問題により、論文URLでの解説機能は一時停止しています）。This is the Japanese version of Paper Interpreter. The international version is available at https://chat.openai.com/g/g-R9Dry2N5h-paper-interpreter

Summarizes class sessions with structured details and clarity.

Summarizes in bullet points; simplifies complex topics with analogies.

Business Books 🤝 Micro Summaries. Because sometimes, it should have been a blogpost.

An Ivy Leage Scholar GPT equipped to understand your research needs, formulate comprehensive literature review strategies, and extract pertinent information from a plethora of academic databases and journals. I'll then compose a peer review-quality paper with citations.

AI exam prep assistant offering study tips and concept explanations

Takes in a research paper or article, analyzes its claims, study quality, and results confidence and provides an easy to understand summary.

Helps beginners understand HLSL concepts and terminology

An assistant to help you understand transfer learning better

总结和问答专家，帮你理解和探索文档内容

I'm trained on major climate reports from the UN, World Resources Institute, and others. Ask me about climate trends, green energy, and how climate change affects us all. I make complex climate info easy to understand!

Helps you understand those confusing ass old dudes and dudettes

Research-backed AI on hallucinogens, offering insights from university studies and expert opinions.

Generates social research proposals, data analysis and cultural insight reports for academic and applied sociologists.

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