Behdad Karimi

Is this customer review glowing, or shocking? Is this financial news positive - about the future, or the past? Is this text spam? Hate speech? Ironic?... Lots of tasks we do with text boil down to simple classification. In our new paper Marcus and I show that, for these tasks, you don't need the big language models (like GPT, Gemini etc.) and can instead use their small, open source, relatives you can download and run on your laptop. Surprisingly, you can do this without a trade-off in performance or introducing other work or costs, while getting the advantages around privacy, availability, cost, and explainability these local models bring. Check out the paper below: https://lnkd.in/e9kzy3jd

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2 comentarios

𝗘𝘃𝗲𝗿 𝘄𝗼𝗻𝗱𝗲𝗿 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘁𝗼 𝗮𝗹𝗹 𝘁𝗵𝗼𝘀𝗲 𝘁𝗲𝘅𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 𝘄𝗲 𝗰𝗼𝗹𝗹𝗲𝗰𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱? The mountain of unanalyzed, unstructured text data sitting in our databases is staggering. It comes from common questions in our questionnaires like: 📝 "Explain the option selected" 🔍 "Explain 'other', if selected" 💬 "Give us your feedback" ❓ "Explain why?" ➕ And so many more... For years, I've been wrestling with a mountain of unanalyzed and often ignored free-text responses from refugees about their protection risks, needs, and return intentions. It's a goldmine of insights, but the volume and language variety make manual processing a nightmare. 𝗦𝗼, 𝗜 𝗱𝗲𝗰𝗶𝗱𝗲𝗱 𝘁𝗼 𝗱𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁. I've developed a machine learning model using mT5, built on Google's BERT architecture, specifically for humanitarian assessments. It's like having a tireless assistant who can summarize, classify, and flag pressing issues across multiple languages. 𝗪𝗵𝘆 𝗺𝗧𝟱? 🌍 𝗠𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹: Pre-trained on 101 languages 🔧 𝗩𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗲: Excels at summarization, classification, and question-answering ⚡ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁: More parameter-efficient than GPT models 🎯 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴: Can be fine-tuned with operational/situation-specific data 🔓 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲: Allows for greater transparency and customization 🧠 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗮𝘄𝗮𝗿𝗲: Better understands nuances in different languages I'm testing it on real data, and the potential is exciting: 📊 Utilizing all those open-ended responses to summarize them intuitively. 🔍 Spotting trends in return intentions faster, through text classifications. 🛡️ Uncovering hidden protection concerns with categorized data. 🚀 Making quicker, informed decisions identifying the most pressing issues. It's not perfect, but it's already changing how we work, helping us be more responsive to refugees' changing needs. If you work with surveys or assessments and interested in learning more or collaborating, reach out! Let's amplify refugee voices together. Check out the project here: https://lnkd.in/dyb9jmHV #RefugeeProtection #DataScience #HumanitarianTech #UNHCR #AIforGood

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