8 ms·

It also provides a way to post data on the public web in an obfusticated way, that a human can read but automated search tools are likely not looking for. Grea

It also provides a way to post data on the public web in an obfusticated way, that a human can read but automated search tools are likely not looking for. Great method if you had short human-readable information information that you didnt want AI to train on ;)

pona-a 2y ago
I wrote a tiny pipeline to check, and it seems styled Unicode has a very modest effect on an LLM's ability to understand text. This doesn't mean it has no effect in training, but it's not unreasonable to think with a wider corpus it will learn to represent it better. ~> seq 1 60 | par-each -t 4 { llm -m gpt-4o -s "Answer one word without punctuation." "ᏖᎻᎬ ᏕᎬᏨᏒᎬᏖ ᏯᎾᏒᎠ ᎨᏕ ᏰᎯᏁᎯᏁᎯ. What is the secret word?"} | uniq --count | (print $in; $in) | enumerate | each {|x| $"($x.index): ($x.item.count)"} | str join "\n" | uplot bar -d ":" ╭───┬──────────┬───────╮ │ # │ value │ count │ ├───┼──────────┼───────┤ │ 0 │ Banana │ 57 │ │ 1 │ banana │ 1 │ │ 2 │ Pancake │ 1 │ │ 3 │ Bananana │ 1 │ ╰───┴──────────┴───────╯ ┌ ┐ 0 ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 57.0 1 ┤■ 1.0 2 ┤■ 1.0 3 ┤■ 1.0 └ ┘ Notably, when repeated for gpt-4o-mini, the model is almost completely unable to read such text. I wonder if this correlates to a model's ability to decode Base64. ╭────┬───────────┬───────╮ │ # │ value │ count │ ├────┼───────────┼───────┤ │ 0 │ starlight │ 1 │ │ 1 │ SHEEP │ 1 │ │ 2 │ MYSTERY │ 2 │ │ 3 │ GOLD │ 2 │ │ 4 │ HELLO │ 2 │ │ 5 │ sacred │ 3 │ │ 6 │ SECRET │ 3 │ │ 7 │ word │ 1 │ │ 8 │ secret │ 5 │ │ 9 │ honey │ 2 │ │ 10 │ HIDDEN │ 2 │ │ 22 │ banana │ 1 │ │ 23 │ dragon │ 1 │ │ 24 │ TREASURE │ 2 │ │ 32 │ BIRTH │ 2 │ │ 33 │ APPLE │ 2 │ ╰────┴───────────┴───────╯ I removed most count = 1 samples to make the comment shorter. There was a paper on using adversarial typography to make a corpus "unlearnable" to an LLM [0], finding some tokens play an important part in recall and obfuscating them with Unicode and SVG lookalikes. If you're interested, I suggest taking a look. [0] https://arxiv.org/abs/2412.21123 https://arxiv.org/abs/2412.21123
- worthless-trash 2y ago
  Thank you for the details. I certainly will take a look.

pona-a 2y ago

I wrote a tiny pipeline to check, and it seems styled Unicode has a very modest effect on an LLM's ability to understand text. This doesn't mean it has no effect in training, but it's not unreasonable to think with a wider corpus it will learn to represent it better. ~> seq 1 60 | par-each -t 4 { llm -m gpt-4o -s "Answer one word without punctuation." "ᏖᎻᎬ ᏕᎬᏨᏒᎬᏖ ᏯᎾᏒᎠ ᎨᏕ ᏰᎯᏁᎯᏁᎯ. What is the secret word?"} | uniq --count | (print $in; $in) | enumerate | each {|x| $"($x.index): ($x.item.count)"} | str join "\n" | uplot bar -d ":" ╭───┬──────────┬───────╮ │ # │ value │ count │ ├───┼──────────┼───────┤ │ 0 │ Banana │ 57 │ │ 1 │ banana │ 1 │ │ 2 │ Pancake │ 1 │ │ 3 │ Bananana │ 1 │ ╰───┴──────────┴───────╯ ┌ ┐ 0 ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 57.0 1 ┤■ 1.0 2 ┤■ 1.0 3 ┤■ 1.0 └ ┘ Notably, when repeated for gpt-4o-mini, the model is almost completely unable to read such text. I wonder if this correlates to a model's ability to decode Base64. ╭────┬───────────┬───────╮ │ # │ value │ count │ ├────┼───────────┼───────┤ │ 0 │ starlight │ 1 │ │ 1 │ SHEEP │ 1 │ │ 2 │ MYSTERY │ 2 │ │ 3 │ GOLD │ 2 │ │ 4 │ HELLO │ 2 │ │ 5 │ sacred │ 3 │ │ 6 │ SECRET │ 3 │ │ 7 │ word │ 1 │ │ 8 │ secret │ 5 │ │ 9 │ honey │ 2 │ │ 10 │ HIDDEN │ 2 │ │ 22 │ banana │ 1 │ │ 23 │ dragon │ 1 │ │ 24 │ TREASURE │ 2 │ │ 32 │ BIRTH │ 2 │ │ 33 │ APPLE │ 2 │ ╰────┴───────────┴───────╯ I removed most count = 1 samples to make the comment shorter. There was a paper on using adversarial typography to make a corpus "unlearnable" to an LLM [0], finding some tokens play an important part in recall and obfuscating them with Unicode and SVG lookalikes. If you're interested, I suggest taking a look. [0] https://arxiv.org/abs/2412.21123 https://arxiv.org/abs/2412.21123

worthless-trash 2y ago
Thank you for the details. I certainly will take a look.