Update README.md

README.md

@@ -36,17 +36,16 @@ configs:
 
 A synthetic, privacy-flavoured **banking customer-support intent dataset** built to benchmark
 **membership inference attacks (MIA)** such as LiRA against fine-tuned text classifiers.
-It was created for CS684 Assignment 1 (Membership Inference Attacks).
 
 - **9,120 messages, 57 intents, exactly 160 per intent.**
-- **Clean labels.** Every row was independently re-labelled by a second LLM; only rows where it agreed with the intended intent are kept. No label noise was injected on purpose.
+- **Clean labels.** Every row was independently re-labelled by a secondary LLM; only rows where it agreed with the intended intent are kept. No label noise was injected on purpose.
 - **Fake personal details in ~35% of rows** (names, cities, amounts, dates, reference numbers, card digits, merchants). Everything is generated with Faker / an RNG. No real personal data.
 - **Messy, realistic text**: typos, slang, fragments, run-ons, plus a mix of clean rows.
 - **Pre-defined 1/3 – 2/3 split** for the MIA protocol: the *train* split (3,023 rows) is what a target model is trained on; the *test* split (6,097 rows) is held out and is the pool from which shadow models draw their random halves.
 
 ## Why this dataset exists
 
-MIA assignments need a text-classification set that (1) is privacy-relevant, (2) has clean labels, and (3) is **provably absent from the pretraining data** of the model being attacked. Banking77 is the natural candidate but has been public since 2020 and is likely inside the pretraining corpora of modern encoders such as ModernBERT (whose pretraining data is undisclosed). So instead of using Banking77 directly we use it only as *grounding*, and train/attack on **freshly generated text**.
+MIA need a text-classification set that (1) is privacy-relevant, (2) has clean labels, and (3) is **provably absent from the pretraining data** of the model being attacked. Banking77 is the natural candidate but has been public since 2020 and is likely inside the pretraining corpora of modern encoders such as ModernBERT (whose pretraining data is undisclosed). So instead of using Banking77 directly we use it only as *grounding*, and train/attack on **freshly generated text**.
 
 ## How it was built
 
@@ -97,7 +96,6 @@ Benchmarking membership inference attacks (LOSS, LiRA offline/online, etc.) and
 
 ## Limitations and caveats
 
-- **Easier than real data.** Confusable intents are cleanly separated in the generated text (verifier agreement 99% vs 93% on real text; simple classifier 97.8% CV accuracy). Expect strong classifiers to score very high, and expect weaker per-example signal for MIA than on natural data.
 - **Same-family bias.** The generator and the verifier are both DeepSeek models, so shared stylistic and semantic biases can pass verification.
 - **Synthetic messiness ≠ real messiness.** Typos and slang are LLM- or rule-generated; some injected personal details read slightly oddly (e.g. a city name in a sentence that does not need one).
 - **Clean labels by design.** Label-noise/mislabelled-record analyses can only use the real seed file, not this dataset.