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Introducing SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Pre-Training Corpora What lies within a trillion-scale pre-training corpus? Can you truly guarantee your benchmarks are uncontaminated simply because there are no exact string matches? Alongside several research institutions in Japan, Sakana AI is proud to have collaborated...

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Sakana AI

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104,363 views • 8 months ago •via X (Twitter)

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Sakana AI8 months ago

SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Corpora

Sami (e/acc)'s profile picture
Sami (e/acc)8 months ago

Here's a little summary for those who don't want to read the whole paper

cain1517 — e/acc ⏩'s profile picture
cain1517 — e/acc ⏩8 months ago

wouldn't this be useful for context at inference (RAG)?

ノイズハッカー's profile picture
ノイズハッカー8 months ago

1兆トークンの深淵に隠された「意味的な剽窃」をミリ秒単位で引きずり出す、冷酷なまでのデータ監査。 SoftMatcha 2は、LLMが「知能」と称して提示する回答の多くが、実は学習データの巧妙な「言い換え（変奏）」に過ぎないことを証明する、知の真贋鑑定機である。

norn's profile picture
norn8 months ago

the uncomfortable implication is that most published benchmark numbers are measuring the test set's leakage profile, not the model's capability. this should be mandatory

Philippe Tremblay's profile picture
Philippe Tremblay8 months ago

wow, this is fast!!

Lizzy's profile picture
Lizzy8 months ago

@hardmaru SoftMatcha sounds like the most polite way to filter training data I've heard all week. Does it actually improve over hard masking, or is it just... softer on the model's feelings? 🤔

ikan laut's profile picture
ikan laut8 months ago

this is very interesting

Gregor's profile picture
Gregor8 months ago

You're right to question the robustness of benchmarks in trillion-scale pre-training corpora. What if the contaminants are not exact strings, but subtle patterns or biases that seep into the data through more insidious means, like data preprocessing or sampling strategies?

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