Cleanlab

One of the largest financial institutions in the world, BBVA, uses Cleanlab to improve their categorization of all financial transactions. Results achieved *without having to change their current model*: ➡️ Reduced labeling effort by 98% ➡️ Improved model accuracy by 28% This is the power of #DataCentricAI tools that provide automation to improve your data: Your existing (and future) models improve immediately with better data! Start practicing automated data improvement: https://cleanlab.ai/studio

The CEO of Cohere on what totally transformed his understanding over the last few months: The importance of data quality for reliable AI (even a single bad example amongst billions matters) "It's surreal how sensitive the models are to their data, everyone underrates it" https://lnkd.in/gM2MDBfj

This week is #ACL, the top research conference in #NLP & #GenAI. Our scientists are presenting a paper on the state-of-the-art method to detect bad/incorrect LLM outputs (hallucinations). This uncertainty estimation technique can apply to text generated from any LLM API or Agent, including custom models that your company trained. Quantifying both aleatoric and epistemic uncertainty in the model, our method can auto-detect when prompts are overly complex/vague or when they are anomalous (very different than the data the LLM was trained on). Read our paper to learn how to achieve more reliable AI: https://lnkd.in/dgSrYP3r

Are you considering switching LLMs but dreading another round of LLM Evaluations and then resolving discrepancies between reviewers? Our open-source CROWDLAB software optimally combines human & AI  to help you achieve more accurate LLM Evals with less manual review. You can use CROWDLAB with any LLM combined with an arbitrary number of human reviewers per example. See our latest blog post and tutorial, where we apply CROWDLAB to the famous MT-Bench LLM evaluation dataset (with one to five reviewers per evaluation!), showing how to determine: the optimal consensus rating for each example, which examples warrant additional human review, and which reviewers are most/least reliable overall: https://lnkd.in/gk5CrAAr

Announcing a new quickstart tutorial for Cleanlab Studio: Building Cheaper and More Effective RAG with Cleanlab! What is Retrieval-Augmented Generation (RAG)? RAG combines the retrieval of relevant documents with generative AI to create context-rich responses. By generating responses based on retrieved documents, RAG can significantly enhance accuracy and quickly produce relevant answers. While RAG represents a major advancement in GenAI, many companies struggle to productionize it due to messy data and unreliable AI. Cleanlab Studio steps in to solve these problems. Here’s how: 1️⃣ Data Cleaning: Cleanlab Studio cleans your document data by removing duplicates, near duplicates, PII, low-quality/unsafe content, and non-English text. It supports directories of heterogeneous document types. 2️⃣ Smart Tagging: Our AI adds tags with smart metadata labels that improve retrieval, making it easier and faster to find relevant information. 3️⃣ Trustworthiness Reporting: Cleanlab Studio reports the trustworthiness of every LLM-generated response, allowing your team to set your own thresholds for queries to automate with confidence. Check out our video tutorial to see how Cleanlab Studio can elevate your RAG applications and drive success in your organization. 👉 https://lnkd.in/gJnhr_ip

🔍 How can you automatically detect errors in LLM outputs? Check out this interesting study by HumanSignal (creators of the data labeling software Label Studio) Exploring LLMs for data labeling, their study evaluates 4 error-detection techniques: Token Probabilities LLM-as-Judge Self-Consistency Cleanlab’s Confident Learning algorithm Their Conclusion: "Among the techniques, the confident learning approach, facilitated by Cleanlab, showed the most promising results when precision should be maximized. This method excels by providing out-of-sample probability estimates, which are crucial for identifying mislabelings more accurately.” Next Steps: In addition to Confident Learning, Cleanlab scientists also invented the Trustworthy Language Model. TLM is a state-of-the-art system to automatically catch untrustworthy LLM answers, especially in more open-ended settings where LLMs often hallucinate false facts. Confident Learning and TLM have helped countless teams finally achieve reliable AI. Adopt Cleanlab’s automated techniques to prevent errors in both your model outputs and inputs.

Product Announcement: Introducing Cleanlab Studio's Auto-Labeling Agent! Is your team bogged down by the tedious task of manual data labeling? Are your algorithms struggling with limited examples? Cleanlab Studio’s Auto-Labeling Agent is designed to alleviate these challenges by efficiently suggesting accurate new labels to complete your dataset effortlessly. 🔍 Why It Matters: Fully automated annotation can overlook important nuances, while fully manual annotation is both error-prone and labor-intensive. By blending human and automated efforts, our Auto-Labeling Agent enhances the accuracy and efficiency of data annotation, making the process seamless and significantly quicker. These AI-suggested labels mean humans only need to focus on the rows where their manual efforts have the highest ROI. ⚙️ How It Works: Simply import a dataset with less than 50% labels, and our Auto-Labeling Agent will automatically provide high-confidence suggestions for the remaining rows. This approach allows for streamlined and rapid iterations while keeping you in full control. Our pilot users have experienced an 80% reduction in time spent on labeling and iterations. Ready to put your annotation on cruise control? 🏎️💨 Read our blog for more details and sign up for Cleanlab Studio today – it’s free to try, with no code required. Learn more: https://lnkd.in/gfQqtjm9 Sign up for Cleanlab Studio: https://app.cleanlab.ai/

Despite rapid recent advances in Foundation models, most enterprises still struggle to deliver value with AI. Models became cheaper/faster, but remain fundamentally unreliable and prone to hallucination. Developed through years of hard research, Cleanlab software tackles this challenge head on. Today we're honored to be featured amongst the Top 5 AI Hallucination Detection Solutions https://lnkd.in/gHAJPpYR Cleanlab adds trust to every input and output of your GenAI solutions, so you can finally achieve reliable AI ✨

Want to improve LLM trustworthiness? Check out this innovative approach! 🌟 In the evolving AI landscape, ensuring language model reliability is crucial. One promising method is agent self-reflection and correction, explored using Cleanlab Trust LLM with LlamaIndex introspective agent framework. What is agent self-reflection and correction? 🤔 AI agents critically evaluate and refine their outputs to meet trustworthiness thresholds, ensuring more accurate information. Why is this important? 🌟 - Mitigating Hallucination: Reduces factually incorrect outputs. - Enhancing Trustworthiness: Improves output reliability, crucial for healthcare, finance, and legal fields. - Iterative Improvement: Promotes continuous learning and robustness. - Transparency: Ensures clear criteria for corrections and accuracy. Practical Example 🛠️ Using Cleanlab and Llama Index, I developed a tool-interactive reflection agent. It effectively reduces errors, as demonstrated by correcting misleading statements about nutrition. Find implementation details and code in my GitHub repository and read the research paper "CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing." Looking Ahead 🚀 Integrating self-reflection in LLMs is a major AI advancement. As we refine these techniques, expect more reliable and trustworthy AI systems. Check out the notebook! https://lnkd.in/ehEWBJh3 #AI #MachineLearning #DataScience #LLM #ArtificialIntelligence #TrustworthyAI #Innovation #Cleanlab #LlamaIndex

Tomorrow! Join us for a Sake Social in SF with Cleanlab and @_odsc! 🍶✨ Let's toast to AI with a data curation keynote from @cgnorthcutt and enjoy some delicious sake. Don't miss out on this opportunity to network and learn from industry experts. RSVP: https://bit.ly/3RQlPmw