Best AI tools for< Benchmark Automatic Evaluation >

HelloData is an AI-powered multifamily market analysis platform that automates market surveys, unit-level rent analysis, concessions monitoring, and development feasibility reports. It provides financial analysis tools to underwrite multifamily deals quickly and accurately. With custom query builders and Proptech APIs, users can analyze and download market data in bulk. HelloData is used by over 15,000 multifamily professionals to save time on market research and deal analysis, offering real-time property data and insights for operators, developers, investors, brokers, and Proptech companies.

Particl is an AI-powered platform that automates competitor intelligence for modern retail businesses. It provides real-time sales, pricing, and sentiment data across various e-commerce channels. Particl's AI technology tracks sales, inventory, pricing, assortment, and sentiment to help users quickly identify profitable opportunities in the market. The platform offers features such as benchmarking performance, automated e-commerce intelligence, competitor research, product research, assortment analysis, and promotions monitoring. With easy-to-use tools and robust AI capabilities, Particl aims to elevate team workflows and capabilities in strategic planning, product launches, and market analysis.

Seek AI is a generative AI-powered database query tool that helps businesses break through information barriers. It is the #1 most accurate model on the Yale Spider benchmark and offers a variety of features to help businesses modernize their analytics, including auto-verification with confidence estimation, natural language summary, and embedded AI data analyst.

MagicBrief is the world's most advanced tool for creative research and analytics, empowering teams to launch winning ad campaigns. With features like MagicAI powered ad search engine, powerful AI search, theme filters, and AI scripts, MagicBrief helps users cut creative research time, monitor competitor activity, and identify top-performing ads. The application provides insights into ad account performance, automates reporting, and offers AI scoring framework for creative benchmarks. Trusted by over 5,000 teams, MagicBrief enhances creative output, speeds up work, and enables efficient collaboration between media buyers and creative teams.

WorkViz is an AI-powered performance tool designed for remote teams to visualize productivity, maximize performance, and foresee the team's potential. It offers features such as automated daily reports, employee voice expression through emojis, workload management alerts, productivity solutions, and intelligent summaries. WorkViz ensures data security through guaranteed audit, desensitization, and SSL security protocols. The application has received positive feedback from clients for driving improvements, providing KPIs and benchmarks, and simplifying daily reporting. It helps users track work hours, identify roadblocks, and improve team performance.

Junbi.ai is an AI-powered insights platform designed for YouTube advertisers. It offers AI-powered creative insights for YouTube ads, allowing users to benchmark their ads, predict performance, and test quickly and easily with fully AI-powered technology. The platform also includes expoze.io API for attention prediction on images or videos, with scientifically valid results and developer-friendly features for easy integration into software applications.

SeeMe Index is an AI tool for inclusive marketing decisions. It helps brands and consumers by measuring brands' consumer-facing inclusivity efforts across public advertisements, product lineup, and DEI commitments. The tool utilizes responsible AI to score brands, develop industry benchmarks, and provide consulting to improve inclusivity. SeeMe Index awards the highest-scoring brands with an 'Inclusive Certification', offering consumers an unbiased way to identify inclusive brands.

ARC Prize is a platform hosting a $1,000,000+ public competition aimed at beating and open-sourcing a solution to the ARC-AGI benchmark. The platform is dedicated to advancing open artificial general intelligence (AGI) for the public benefit. It provides a formal benchmark, ARC-AGI, created by François Chollet, to measure progress towards AGI by testing the ability to efficiently acquire new skills and solve open-ended problems. ARC Prize encourages participants to try solving test puzzles to identify patterns and improve their AGI skills.

Report Card AI is an AI Writing Assistant that helps users generate high-quality, unique, and personalized report card comments. It allows users to create a quality benchmark by writing their first draft of comments with the assistance of AI technology. The tool is designed to streamline the report card writing process for teachers, ensuring error-free and eloquently written comments that meet specific character count requirements. With features like 'rephrase', 'Max Character Count', and easy exporting options, Report Card AI aims to enhance efficiency and accuracy in creating report card comments.

Perspect is an AI-powered platform designed for high-performance software teams. It offers real-time insights into team contributions and impact, optimizing developer experience, and rewarding high-performers. With 50+ integrations, Perspect enables visualization of impact, benchmarking performance, and uses machine learning models to identify and eliminate blockers. The platform is deeply integrated with web3 wallets and offers built-in reward mechanisms. Managers can align resources around crucial KPIs, identify top talent, and prevent burnout. Perspect aims to enhance team productivity and employee retention through AI and ML technologies.

Gorilla is an AI tool that integrates a large language model (LLM) with massive APIs to enable users to interact with a wide range of services. It offers features such as training the model to support parallel functions, benchmarking LLMs on function-calling capabilities, and providing a runtime for executing LLM-generated actions like code and API calls. Gorilla is open-source and focuses on enhancing interaction between apps and services with human-out-of-loop functionality.

Trend Hunter is an AI-powered platform that offers a wide range of services to accelerate innovation and provide insights into trends and opportunities. With a vast database of ideas and innovations, Trend Hunter helps individuals and organizations stay ahead of the curve by offering trend reports, newsletters, training programs, and custom services. The platform also provides personalized assessments to enhance innovation potential and offers resources such as books, keynotes, and online courses to foster creativity and strategic thinking.

JaanchAI is an AI-powered tool that provides valuable insights for e-commerce businesses. It utilizes artificial intelligence algorithms to analyze data and trends in the e-commerce industry, helping businesses make informed decisions to optimize their operations and increase sales. With JaanchAI, users can gain a competitive edge by leveraging advanced analytics and predictive modeling techniques tailored for the e-commerce sector.

The Deepfake Detection Challenge Dataset is a project initiated by Facebook AI to accelerate the development of new ways to detect deepfake videos. The dataset consists of over 100,000 videos and was created in collaboration with industry leaders and academic experts. It includes two versions: a preview dataset with 5k videos and a full dataset with 124k videos, each featuring facial modification algorithms. The dataset was used in a Kaggle competition to create better models for detecting manipulated media. The top-performing models achieved high accuracy on the public dataset but faced challenges when tested against the black box dataset, highlighting the importance of generalization in deepfake detection. The project aims to encourage the research community to continue advancing in detecting harmful manipulated media.

UserTesting is a Human Insight Platform that allows organizations to quickly gain a first-person understanding of customer experiences, enabling them to build greater customer empathy. The platform offers comprehensive testing capabilities, insights identification, performance measurement, and insights sharing across organizations. UserTesting empowers users to run tests for free, see what customers experience, and turn feedback into better designs efficiently. With features like AI Insights Hub, integrations, mobile testing, and templates, UserTesting helps users target diverse audiences, validate findings confidently, measure and benchmark performance, and boost consumer trust. Trusted by leading brands, UserTesting provides human insights that drive innovation, improve customer experiences, and enhance product development.

Clarity AI is an AI-powered technology platform that offers a Sustainability Tech Kit for sustainable investing, shopping, reporting, and benchmarking. The platform provides built-in sustainability technology with customizable solutions for various needs related to data, methodologies, and tools. It seamlessly integrates into workflows, offering scalable and flexible end-to-end SaaS tools to address sustainability use cases. Clarity AI leverages powerful AI and machine learning to analyze vast amounts of data points, ensuring reliable and transparent data coverage. The platform is designed to empower users to assess, analyze, and report on sustainability aspects efficiently and confidently.

Unify is an AI tool that offers a unified platform for accessing and comparing various Language Models (LLMs) from different providers. It allows users to combine models for faster, cheaper, and better responses, optimizing for quality, speed, and cost-efficiency. Unify simplifies the complex task of selecting the best LLM by providing transparent benchmarks, personalized routing, and performance optimization tools.

Groq is a fast AI inference tool that offers GroqCloud™ Platform and GroqRack™ Cluster for developers to build and deploy AI models with ultra-low-latency inference. It provides instant intelligence for openly-available models like Llama 3.1 and is known for its speed and compatibility with other AI providers. Groq powers leading openly-available AI models and has gained recognition in the AI chip industry. The tool has received significant funding and valuation, positioning itself as a strong challenger to established players like Nvidia.

ASK BOSCO® is an AI reporting and forecasting platform designed for agencies and retailers. It helps users collect and analyze data to improve decision-making, budget planning, and forecasting accuracy. The platform offers features such as AI reporting, competitor benchmarking, AI budget planning, and data integrations to streamline marketing processes and enhance performance. Trusted by leading brands and agencies, ASK BOSCO® provides personalized insights and recommendations to optimize media spend and drive revenue growth.

Hailo Community is an AI tool designed for developers and enthusiasts working with Raspberry Pi and Hailo-8L AI Kit. The platform offers resources, benchmarks, and support for training custom models, optimizing AI tasks, and troubleshooting errors related to Hailo and Raspberry Pi integration.

This repository contains data and code for the experiments in the SummHay paper. It includes publicly released Haystacks in conversational and news domains, along with scripts for running the pipeline, visualizing results, and benchmarking automatic evaluation. The data structure includes topics, subtopics, insights, queries, retrievers, summaries, evaluation summaries, and documents. The pipeline involves scripts for retriever scores, summaries, and evaluation scores using GPT-4o. Visualization scripts are provided for compiling and visualizing results. The repository also includes annotated samples for benchmarking and citation information for the SummHay paper.

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)

LLM-as-a-Judge is a repository that includes papers discussed in a survey paper titled 'A Survey on LLM-as-a-Judge'. The repository covers various aspects of using Large Language Models (LLMs) as judges for tasks such as evaluation, reasoning, and decision-making. It provides insights into evaluation pipelines, improvement strategies, and specific tasks related to LLMs. The papers included in the repository explore different methodologies, applications, and future research directions for leveraging LLMs as evaluators in various domains.

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 focuses on test-time computing, exploring various strategies such as test-time adaptation, modifying the input, editing the representation, calibrating the output, test-time reasoning, and search strategies. It covers topics like self-supervised test-time training, in-context learning, activation steering, nearest neighbor models, reward modeling, and multimodal reasoning. The repository provides resources including papers and code for researchers and practitioners interested in enhancing the reasoning capabilities 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.

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

Rageval is an evaluation tool for Retrieval-augmented Generation (RAG) methods. It helps evaluate RAG systems by performing tasks such as query rewriting, document ranking, information compression, evidence verification, answer generation, and result validation. The tool provides metrics for answer correctness and answer groundedness, along with benchmark results for ASQA and ALCE datasets. Users can install and use Rageval to assess the performance of RAG models in question-answering tasks.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

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.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

Evalchemy is a unified and easy-to-use toolkit for evaluating language models, focusing on post-trained models. It integrates multiple existing benchmarks such as RepoBench, AlpacaEval, and ZeroEval. Key features include unified installation, parallel evaluation, simplified usage, and results management. Users can run various benchmarks with a consistent command-line interface and track results locally or integrate with a database for systematic tracking and leaderboard submission.

Awesome-LLM-Eval: a curated list of tools, benchmarks, demos, papers for Large Language Models (like ChatGPT, LLaMA, GLM, Baichuan, etc) Evaluation on Language capabilities, Knowledge, Reasoning, Fairness and Safety.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

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.

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.

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.

Analyzes contracts vs benchmarks, grades alignment

Benchmark HVAC GPT model with unmatched expertise and forward-thinking solutions, powered by OpenAI

A strategic SaaS analyst for CXOs, with a focus on market trends and benchmarks.

ESG performance assessment and reporting guide.

Compensation Advisor for HR and managers (v0.4)

Quizzes users on advanced machine learning concepts.

Guides businesses in managing global tax liabilities efficiently.

Free to use oracle for analyzing Net Promoter Score data.

I provide monthly salary data in euros, using a structured format for global job roles.

Ensures software performance meets organizational standards and expectations.