Toloka

🚀 Exciting News: Toloka and Top Universities Launch Innovative Benchmark for Detecting AI-Generated Texts! We’re thrilled to announce a groundbreaking collaboration between the University of Oslo, Penn State University, and Toloka, unveiling Beemo, a cutting-edge benchmark to revolutionize AI text detection. This new benchmark, created by experts from leading institutions, offers a robust, realistic testing environment for AI text detectors. Beemo is designed using LLMs like LLaMA and expert human annotators, challenging detectors to differentiate between purely machine-generated texts and human-edited ones, reflecting real-world scenarios. Why is this important? Detecting AI-generated content is crucial for:  1️⃣ Maintaining data integrity, 2️⃣ Addressing ethical and legal concerns, 3️⃣ Enhancing the reliability of AI systems. Adaku Uchendu from MIT Lincoln Labs emphasizes the importance of distinguishing artificial texts from human-written ones to protect the integrity of our information ecosystem. Meanwhile, Preslav Nakov from MBZUAI (Mohamed bin Zayed University of Artificial Intelligence) highlights the challenge of detecting hybrid texts co-authored by humans and AI, as they can be particularly deceptive. With contributions from top NLP researchers such as Vladislav Mikhailov, Saranya Venkatraman, Jason Lucas, M.Sc., MPH, Ph.D (cand), MPH, Ph.D (cand), Jooyoung Lee, and more. As AI evolves, this benchmark is a vital tool for NLP practitioners and researchers. It sets new standards for AI-generated content detection and paves the way for future innovations. Beemo is now available for public use on: GitHub: https://lnkd.in/dksfBKFD Hugging Face: https://lnkd.in/dp4db-gt Let’s continue pushing the boundaries of AI together! Read the full blog - link in the comments! Ekaterina Artemova Natalia Fedorova

We are pleased to continue sharing insights from our participation at #ICML2024 in Vienna. A notable research paper by Alexander Wettig, Aatmik Gupta, Saumya Malik, and Danqi Chen has garnered our attention for its exploration of high-quality data selection in language model training. The authors present a novel approach that encapsulates human intuition on data quality by focusing on four key factors: writing style, required expertise, factual accuracy, and educational value. By leveraging language models to perform pairwise comparisons of texts and translating these judgments into scalar values, they propose an efficient method for selecting superior data for model training. Their findings highlight the importance of balancing data quality with diversity, demonstrating that models trained with this approach achieve lower perplexity and improved in-context learning performance compared to traditional methods. This research represents a significant advancement in optimizing language model training, and we extend our gratitude to the authors for their valuable contributions. Read the full paper: https://lnkd.in/dVi3YSgY #ArtificialIntelligence #MachineLearning #LLMs #genAI

Inter-rater reliability has been believed to be an important factor in ensuring data quality for AI and machine learning projects, but there are better ways to ensure data quality.📊 In our latest blog, we cover: 💡 What is Inter-Rater Reliability (IRR)?: A fundamental concept that measures the level of agreement among different annotators working on the same data set. 💡 Why IRR matters: Reliable data annotations are vital for training accurate and dependable AI models. Consistency in labeling can impact the performance of your algorithms. 💡 How to measure IRR: We discuss various methods such as Cohen's Kappa, Fleiss' Kappa, and Krippendorff's Alpha, explaining how each technique helps in assessing annotation consistency. 💡 Improving on IRR: Practical strategies and best practices to ensure high-quality data for your AI models. Dive into the full article to learn more: https://bit.ly/4dgMPDH #AI #MachineLearning #DataAnnotation #InterRaterReliability #DataQuality #TolokaAI

🚀 As large language models (LLMs) redefine the AI landscape, the GenAI frontier presents both exciting opportunities and unique challenges. The key to unlocking the full potential of LLMs lies in high-quality Supervised Fine-Tuning (SFT) datasets; think of them as the secret sauce for domain-specific expertise! 🌐 But not all data is created equal. The quest for top-tier SFT data demands expertise, precision, compliance, complexity, and diversity. That’s where Mindrift by Toloka steps in! By harnessing a global network of expert AI Tutors, we ensure that our datasets are not only rich in specialized knowledge but also meet the highest standards of quality and compliance. Ready to conquer the GenAI frontier? Our team of dataset architects is here to help you navigate the journey, from custom SFT datasets to efficient data production pipelines. With the right partner, the quest for SFT data can be seamless and successful. Read the full blog: https://lnkd.in/d7HsE8hv 💡 Let's elevate your AI models together. Talk to us and learn more: https://bit.ly/3YUM67F

🚀 Unleashing the Power of Synthetic Data: Cooking Up Your Own Pipeline for SFT Success 🚀 In the world of AI, high-quality data is the key to unlocking exceptional performance, especially for supervised fine-tuning (SFT). But what if your data isn't enough? That's where synthetic data steps in, offering a game-changing solution to fill in the gaps and boost model accuracy. In our latest blog post, we dive deep into building a custom synthetic data pipeline for SFT. Whether you're a seasoned data scientist or just getting started, this guide covers everything from understanding the basics to implementing and optimizing your pipeline. Learn how to create diverse datasets that can supercharge your machine-learning models! 🔍 What you'll learn: 1️⃣ The importance of synthetic data in SFT. 2️⃣ A step-by-step method to crafting a synthetic data pipeline. 3️⃣ Best practices for ensuring data quality and diversity. 4️⃣ Don't let data limitations hold you back. Explore the full article and start building your synthetic data pipeline today! Link in comments. #AI #MachineLearning #SyntheticData #SupervisedFineTuning #DataScience #TolokaAI

🚀 Exciting news for AI enthusiasts! Our latest blog dives deep into the 155-page research paper "Sparks of AGI" by Microsoft researchers, exploring an early version of GPT-4. 🌟 This groundbreaking technology demonstrates exceptional performance, from answering complex Fermi questions (like "How long would it take to count to one billion?") to assisting with real-world tasks such as repairing a water leak. The paper highlights GPT-4's impressive capabilities across various fields—coding, mathematics, art, music, and even navigating complex social scenarios—suggesting it is a step towards artificial general intelligence (AGI). Read our review to learn more about the achievements and limitations of this emerging technology! 📚💡 Read the full blog: https://bit.ly/3AGnzc7 #AI #AGI #Research #Innovation #Technology #GPT4 #MachineLearning #ArtificialIntelligence

We're excited to share insights from our recent participation at #ICML2024 in Vienna. This is the first post in our series, where we'll highlight some of the research that stood out to us. One of the papers that caught our attention is from the Google DeepMind team, authored by Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, and Benjamin Van Roy. Their work, titled "Efficient Exploration for LLMs" presents innovative strategies for preference data collection. The key takeaway is that it's not necessary to label all data for preference tuning. By focusing on the most valuable data, we can achieve better performance. The team compared four exploration strategies and demonstrated improved outcomes with less data. Thank you to the Google DeepMind team for this valuable research, which is driving innovation in LLM development. We look forward to implementing these strategies in our data production pipelines to generate high-value data for #RLHF. Read the full paper: https://lnkd.in/g5neAz44 #ArtificialIntelligence #MachineLearning #LLMs #genAI

📈 Boost Your GenAI Solutions with Expert Evaluation Services Enhance the performance of your GenAI models with our comprehensive evaluation services, integrating human-in-the-loop feedback and advanced quality metrics: 🌍 Human-in-the-Loop: Evaluate with a trained global crowd or skilled AI tutors via a simple API. 🏅 Golden Benchmarks: Access pre-defined or custom evaluation datasets designed by domain and ML experts. 📊 Extensive Quality Metrics: Leverage a large taxonomy of quality metrics for LLMs, including truthfulness, helpfulness, harmlessness, creativity, structure, and style, tailored to your specific goals. 🚀 Scalable Expertise: Benefit from scalable human insight and expertise of skilled data labelers. 🔍 In-Depth Analysis: Receive detailed evaluation pipelines and comprehensive reports. Optimize your AI solutions with precision and expert insight. Let's take your GenAI performance to new heights! Talk to us and learn more: https://bit.ly/3YUM67F #AI #GenAI #EvaluationServices #HumanInTheLoop #QualityMetrics #DataLabeling #TolokaAI

🌍 The strength of AI lies in its ability to reflect real-world experiences, and Toloka has been at the forefront of bridging technology with human insight. From large-scale data labeling to the launch of our Mindrift platform, we've continuously evolved to meet the changing demands of AI development. Our latest step? Building custom datasets with input from domain experts across fields like medicine, finance, and software development. 🔗 Learn more about how we're pushing the boundaries of AI data from our CEO Olga Megorskaya #AI #DataLabeling #Crowdsourcing #Mindrift #Toloka