Best AI tools for< Improve Scores >

The website offers a comprehensive digital SAT preparation tool that includes full-length tests, AI-powered study plans, and proven learning methods to help students maximize their SAT scores. It provides adaptive testing structure, scoring system, test duration & format, strategic study points, personalized study plans, expert-curated SAT question bank, adaptive full-length practice tests, instant score calculator, and SAT flashcards. The platform aims to enhance accessibility, efficiency, and effectiveness in SAT preparation through a systematic approach designed to boost students' scores by 200+ points.

PrepGenius.ai is an AI-driven test preparation platform designed to revolutionize the way students prepare for AP courses, college admission tests, and more. The platform offers personalized study plans, real-time feedback, interactive learning tools, and comprehensive resources to help students understand their strengths and weaknesses. With PrepGenius.ai, students can study smarter, receive tailored feedback, and track their progress to improve their test scores effectively.

Hubtype is an enterprise chatbot platform that offers customer service automation solutions beyond text-only AI. It provides a messaging-first helpdesk, a drag-and-drop flow builder, and a customer service analytics suite to help businesses create interactive and efficient automated customer experiences. With end-to-end encryption, secure SSL Webhooks, and seamless integration with existing technologies, Hubtype aims to enhance customer satisfaction and drive sales conversions.

Balto is a real-time guidance platform designed for contact centers, powered by AI technology. It offers features such as real-time coaching, QA copilot, notetaking automation, and integrations with various CCaaS systems. Balto aims to improve agent performance, enhance customer experience, ensure compliance, and drive sales growth through behavior change and perfect conversations on every call.

ioni.ai is an AI application that offers ChatGPT-4 solution for customer support. It is a smart chatbot based on the latest AI technology, designed to handle general inquiries, complex questions, and user-specific requests. The application streamlines workflow with immediate responses, brings CSAT scores to a new level, and ensures human-in-the-loop verification for quality control. With self-learning capabilities, ioni.ai constantly improves its responses and provides accurate solutions to customer inquiries.

Sapien ai is an AI-powered tool that helps users transform their notes into powerful quizzes. By simply uploading notes in pdf, docx, or txt format, the tool generates custom quizzes to aid in exam preparation and enhance the learning experience. With Sapien ai, studying becomes more interactive and efficient, making it easier for users to grasp and retain information.

SmartDispute.ai is an AI-powered credit repair application that helps users improve their credit scores by leveraging artificial intelligence technology. The application offers a patented Smart Dispute System™ technology that identifies and helps remove negative accounts from credit reports, making it one of the most effective credit repair systems available. Users can easily repair their own credit with the help of SmartDispute.ai's automated processes and fact-based dispute method. The application provides users with a simple and clearly explained process, allowing them to track their progress and see significant improvements in their credit scores over time.

Blobr is an AI tool designed to optimize Google Ads spending by providing real-time insights and best-in-class PPC practices. It maximizes the return on every dollar spent in Google Ads by offering optimization recommendations through AI agents. Users can automate keyword identification, reduce costs, improve ad quality scores, and experiment with control. Trusted by industry leaders, Blobr helps users save time from repetitive tasks and focus on strategy and innovation.

Dispute AI™ is a credit repair application that leverages Artificial Intelligence to help users fix their credit and increase their credit scores. It automates the process of disputing accounts on all three credit bureaus, provides coaching tutorials, and generates professional letters based on consumer protection laws to effectively remove negative accounts. With Dispute AI™, users can track their results, avoid generic disputes that credit bureaus can reject, and improve their credit scores without the need to hire a credit repair company.

Face Symmetry Test is an AI-powered tool that analyzes the symmetry of facial features by detecting key landmarks such as eyes, nose, mouth, and chin. Users can upload a photo to receive a personalized symmetry score, providing insights into the balance and proportion of their facial features. The tool uses advanced AI algorithms to ensure accurate results and offers guidelines for improving the accuracy of the analysis. Face Symmetry Test is free to use and prioritizes user privacy and security by securely processing uploaded photos without storing or sharing data with third parties.

Convr is a modularized AI underwriting and intelligent document automation workbench that enriches and expedites the commercial insurance new business and renewal submission flow with underwriting insights, business classification and risk scoring. As a trusted technology partner and advisor with deep industry expertise, we help insurance organizations transform their underwriting operations through our AI-driven digital underwriting analysis platform.

How Attractive Am I is an AI-powered tool that analyzes facial features to calculate an attractiveness score. By evaluating symmetry and proportions, the tool provides personalized beauty scores. Users can upload a photo to discover their true beauty potential. The tool ensures accuracy by providing guidelines for taking photos and offers a fun and insightful way to understand facial appeal.

Pitchleague.ai is an AI-powered pitch deck coach that provides slide-by-slide feedback on how to improve your startup's pitch deck. It also scores your deck's investability and allows you to compare your deck to other startups. Pitchleague.ai is used by over 3000 founders and has helped many startups to improve their fundraising success.

InStore.ai is an AI-powered tool designed to monitor, compare, and elevate customer experience across stores. It helps businesses improve store performance by providing key performance scores, proactive guidance, and instant search capabilities to summarize in-store interactions and trends. The tool offers solutions for various industries like fuel & convenience, hospitality, and luxury retail, enabling businesses to understand customer feedback, optimize service, and refine customer interactions. InStore.ai leverages AI to enhance face-to-face experiences for customers and employees, providing timely insights, detailed support, and configurable recommendations tailored to specific audiences.

Pitch Patterns is an AI-powered Quality Control platform designed for teams analyzing sales and customer service calls to enhance close rates and CSAT scores. The platform offers innovative features such as Social Skill Markers, Conversation Analysis, and AI Tracking to provide valuable insights and improve agent performance. With integration capabilities with popular CRM systems like Salesforce and Pipedrive, Pitch Patterns aims to revolutionize call center excellence through AI analytics.

PDF2Quiz is an AI-powered tool that allows users to convert PDF documents into interactive quizzes. Users can upload a PDF, specify the number of questions, select the language, and set the difficulty level to transform the PDF into an engaging quiz. The tool utilizes Optical Character Recognition (OCR) to create quizzes from PDFs with non-selectable text, making it easy for users to assess their knowledge and share quizzes with others. With multilingual quiz conversion capabilities, PDF2Quiz caters to users from various linguistic backgrounds. The tool also offers features such as reviewing scores and answers, challenging users with automatically generated multiple-choice questions, and enabling offline use by saving quizzes and answers as PDFs.

CogniCircuit AI is an AI-powered TOEFL preparation application designed to help students improve their English skills and achieve high scores in the TOEFL exam. The app offers comprehensive practice tests, personalized feedback, and realistic exam simulations to enhance students' reading, speaking, writing, and listening abilities. With over 90,000 students on the platform and a success rate of 98%, CogniCircuit AI is a trusted tool for TOEFL test preparation.

VisualHUB is an AI-powered design analysis tool that provides instant insights on UI, UX, readability, and more. It offers features like A/B Testing, UI Analysis, UX Analysis, Readability Analysis, Margin and Hierarchy Analysis, and Competition Analysis. Users can upload product images to receive detailed reports with actionable insights and scores. Trusted by founders and designers, VisualHUB helps optimize design variations and identify areas for improvement in products.

TOEFL Practice is an AI-powered platform designed to help students prepare for the official TOEFL exam. It offers comprehensive practice materials for all TOEFL test sections, including writing, speaking, reading, and listening. The platform provides real questions, instant feedback, and detailed analytics to boost users' scores. With features like AI-powered feedback, mock tests, and progress tracking, TOEFL Practice aims to empower global learners by connecting students worldwide and helping them achieve their dreams of international education.

Amori is an AI-powered dating coach application that analyzes text conversations to provide relationship insights and compatibility scores. The app helps users understand their relationship dynamics, communication styles, attachment dynamics, moments of intimacy, challenges faced, and more. It offers personalized reports based on GPT-powered algorithms, ensuring privacy by not saving or viewing chats. Amori has been featured in reputable publications like the Wall Street Journal, BuzzFeed, and Cosmopolitan, offering support for various types of relationships beyond just ex-partners.

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.

llm-rankers is a repository that provides implementations for Pointwise, Listwise, Pairwise, and Setwise Document Ranking using Large Language Models. It includes various methods for reranking documents retrieved by a first-stage retriever, such as BM25. The repository offers examples and code snippets for using LLMs to improve document ranking performance in information retrieval tasks. Additionally, it introduces a new setwise reranker called Rank-R1 with reasoning ability.

The repository Adaptive-MT-LLM-Fine-tuning contains code and data for the paper 'Fine-tuning Large Language Models for Adaptive Machine Translation'. It focuses on enhancing Mistral 7B, a large language model, for real-time adaptive machine translation in the medical domain. The fine-tuning process involves using zero-shot and one-shot translation prompts to improve terminology and style adherence. The repository includes training and test data, data processing code, fuzzy match retrieval techniques, fine-tuning methods, conversion to CTranslate2 format, tokenizers, translation codes, and evaluation metrics.

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.

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.

The Lakera PINT Benchmark provides a neutral evaluation method for prompt injection detection systems, offering a dataset of English inputs with prompt injections, jailbreaks, benign inputs, user-agent chats, and public document excerpts. The dataset is designed to be challenging and representative, with plans for future enhancements. The benchmark aims to be unbiased and accurate, welcoming contributions to improve prompt injection detection. Users can evaluate prompt injection detection systems using the provided Jupyter Notebook. The dataset structure is specified in YAML format, allowing users to prepare their datasets for benchmarking. Evaluation examples and resources are provided to assist users in evaluating prompt injection detection models and tools.

MM-RLHF is a comprehensive project for aligning Multimodal Large Language Models (MLLMs) with human preferences. It includes a high-quality MLLM alignment dataset, a Critique-Based MLLM reward model, a novel alignment algorithm MM-DPO, and benchmarks for reward models and multimodal safety. The dataset covers image understanding, video understanding, and safety-related tasks with model-generated responses and human-annotated scores. The reward model generates critiques of candidate texts before assigning scores for enhanced interpretability. MM-DPO is an alignment algorithm that achieves performance gains with simple adjustments to the DPO framework. The project enables consistent performance improvements across 10 dimensions and 27 benchmarks for open-source MLLMs.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

LLaMa2lang is a repository containing convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language that isn't English. The repository aims to improve the performance of LLaMa3 for non-English languages by combining fine-tuning with RAG. Users can translate datasets, extract threads, turn threads into prompts, and finetune models using QLoRA and PEFT. Additionally, the repository supports translation models like OPUS, M2M, MADLAD, and base datasets like OASST1 and OASST2. The process involves loading datasets, translating them, combining checkpoints, and running inference using the newly trained model. The repository also provides benchmarking scripts to choose the right translation model for a target language.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

R-Judge is a benchmarking tool designed to evaluate the proficiency of Large Language Models (LLMs) in judging and identifying safety risks within diverse environments. It comprises 569 records of multi-turn agent interactions, covering 27 key risk scenarios across 5 application categories and 10 risk types. The tool provides high-quality curation with annotated safety labels and risk descriptions. Evaluation of 11 LLMs on R-Judge reveals the need for enhancing risk awareness in LLMs, especially in open agent scenarios. Fine-tuning on safety judgment is found to significantly improve model performance.

Open WebUI Tools Collection is a set of tools for structured planning, arXiv paper search, Hugging Face text-to-image generation, prompt enhancement, and multi-model conversations. It enhances LLM interactions with academic research, image generation, and conversation management. Tools include arXiv Search Tool and Hugging Face Image Generator. Function Pipes like Planner Agent offer autonomous plan generation and execution. Filters like Prompt Enhancer improve prompt quality. Installation and configuration instructions are provided for each tool and pipe.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

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.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

Advanced RAG Techniques is a comprehensive collection of cutting-edge Retrieval-Augmented Generation (RAG) tutorials aimed at enhancing the accuracy, efficiency, and contextual richness of RAG systems. The repository serves as a hub for state-of-the-art RAG enhancements, comprehensive documentation, practical implementation guidelines, and regular updates with the latest advancements. It covers a wide range of techniques from foundational RAG methods to advanced retrieval methods, iterative and adaptive techniques, evaluation processes, explainability and transparency features, and advanced architectures integrating knowledge graphs and recursive processing.

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.

This repository hosts the official code and data artifact for the paper 'Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context'. It introduces Monitor-Guided Decoding (MGD) for code generation using Language Models, where a monitor uses static analysis to guide the decoding. The repository contains datasets, evaluation scripts, inference results, a language server client 'multilspy' for static analyses, and implementation of various monitors monitoring for different properties in 3 programming languages. The monitors guide Language Models to adhere to properties like valid identifier dereferences, correct number of arguments to method calls, typestate validity of method call sequences, and more.

RAGoon is a high-level library designed for batched embeddings generation, fast web-based RAG (Retrieval-Augmented Generation) processing, and quantized indexes processing. It provides NLP utilities for multi-model embedding production, high-dimensional vector visualization, and enhancing language model performance through search-based querying, web scraping, and data augmentation techniques.

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.

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Simulates the IELTS Writing Test, evaluates responses, and estimates band scores.

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