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🧠  𝗖𝗟𝗘𝗔𝗥: 𝗳𝗶𝗿𝘀𝘁 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿𝗴𝗲𝘁 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘄𝗮𝗻𝘁 𝘁𝗵𝗲𝗺 𝘁𝗼 𝗳𝗼𝗿𝗴𝗲𝘁
🧠  𝗖𝗟𝗘𝗔𝗥: 𝗳𝗶𝗿𝘀𝘁 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿𝗴𝗲𝘁 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘄𝗮𝗻𝘁 𝘁𝗵𝗲𝗺 𝘁𝗼 𝗳𝗼𝗿𝗴𝗲𝘁
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With privacy concerns rising, we sometimes need our models to "forget" specific information - like a person's data - while keeping everything else intact. Researchers just released CLEAR, the first benchmark to test how well this works with both text and images.
With privacy concerns rising, we sometimes need our models to "forget" specific information - like a person's data - while keeping everything else intact. Researchers just released CLEAR, the first benchmark to test how well this works with both text and images.
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❌ Bad news: Current methods either fail to truly forget or end up forgetting way too much. It's like trying to remove a single ingredient from a baked cake!
❌ Bad news: Current methods either fail to truly forget or end up forgetting way too much. It's like trying to remove a single ingredient from a baked cake!
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✨ But there's hope: Adding simple mathematical constraints (L1 regularization) during the forgetting process significantly improves results.
✨ But there's hope: Adding simple mathematical constraints (L1 regularization) during the forgetting process significantly improves results.
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🎯 Key insights:
🎯 Key insights:
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✅ The benchmark tests forgetting on 200 fictional personas
✅ The benchmark tests forgetting on 200 fictional personas
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‣ 3,770 visual Q&A pairs
‣ 3,770 visual Q&A pairs
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‣ 4,000 textual Q&A pairs
‣ 4,000 textual Q&A pairs
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‣ Additional real-world tests
‣ Additional real-world tests
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🛑 Most current forgetting methods don't work well with both text and images
🛑 Most current forgetting methods don't work well with both text and images
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‣ They either remember what they should forget
‣ They either remember what they should forget
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‣ Or they forget too much unrelated information
‣ Or they forget too much unrelated information
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✨ Simple mathematical constraints work surprisingly well
✨ Simple mathematical constraints work surprisingly well
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‣ L1 regularization prevents excessive forgetting
‣ L1 regularization prevents excessive forgetting
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‣ Works especially well with the LLMU method
‣ Works especially well with the LLMU method
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👉 Read the full paper here: 
👉 Read the full paper here: 
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https://huggingface.co/papers/2410.18057
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2410.18057
https://huggingface.co/papers/2410.18057
CLEAR: Character Unlearning in Textual and Visual Modalities (2410.18057)
🧠  𝗖𝗟𝗘𝗔𝗥: 𝗳𝗶𝗿𝘀𝘁 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿𝗴𝗲𝘁 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘄𝗮𝗻𝘁 𝘁𝗵𝗲𝗺 𝘁𝗼 𝗳𝗼𝗿𝗴𝗲𝘁

With privacy concerns rising, we sometimes need our models to "forget" specific information - like a person's data - while keeping everything else intact. Researchers just released CLEAR, the first benchmark to test how well this works with both text and images.

❌ Bad news: Current methods either fail to truly forget or end up forgetting way too much. It's like trying to remove a single ingredient from a baked cake!

✨ But there's hope: Adding simple mathematical constraints (L1 regularization) during the forgetting process significantly improves results.

🎯 Key insights:

✅ The benchmark tests forgetting on 200 fictional personas
‣ 3,770 visual Q&A pairs
‣ 4,000 textual Q&A pairs
‣ Additional real-world tests

🛑 Most current forgetting methods don't work well with both text and images
‣ They either remember what they should forget
‣ Or they forget too much unrelated information

✨ Simple mathematical constraints work surprisingly well
‣ L1 regularization prevents excessive forgetting
‣ Works especially well with the LLMU method

👉 Read the full paper here: https://huggingface.co/papers/2410.18057
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John6666 CYGDEN xpgx1