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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...

104,363 Aufrufe • vor 8 Monaten •via X (Twitter)

9 Kommentare

Profilbild von Sakana AI
Sakana AIvor 8 Monaten

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

Profilbild von Sami (e/acc)
Sami (e/acc)vor 8 Monaten

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

Profilbild von cain1517 — e/acc ⏩
cain1517 — e/acc ⏩vor 8 Monaten

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

Profilbild von ノイズハッカー
ノイズハッカーvor 8 Monaten

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。

Profilbild von norn
nornvor 8 Monaten

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

Profilbild von Philippe Tremblay
Philippe Tremblayvor 8 Monaten

wow, this is fast!!

Profilbild von Lizzy
Lizzyvor 8 Monaten

@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? 🤔

Profilbild von ikan laut
ikan lautvor 8 Monaten

this is very interesting

Profilbild von Gregor
Gregorvor 8 Monaten

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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