Upload LEAKAGE_ANALYSIS.md

LEAKAGE_ANALYSIS.md

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+# Train/Test Leakage Analysis
+
+**Date:** 2026-08-05
+**Analyst:** ML Intern automated audit
+**Dataset:** nraptisss/TMF921-intent-to-config-research-sota
+
+## Summary
+
+The journal entry from 2026-04-30 reports "near-duplicate prompt similarity was high" with 602/2,521 test prompts having ≥95% char-ngram similarity to train prompts. **This analysis confirms that this is structural similarity, NOT data leakage.** The OOD splits are scientifically valid.
+
+## Key Findings
+
+### 1. Exact Prompt Overlap: 0% across ALL splits
+
+| Split | Exact Prompt Overlap |
+|---|---|
+| test_in_distribution | 0 / 1,455 (0.0%) |
+| test_template_ood | 0 / 3,503 (0.0%) |
+| test_use_case_ood | 0 / 4,341 (0.0%) |
+| test_sector_ood | 0 / 4,579 (0.0%) |
+| test_adversarial | 0 / 33 (0.0%) |
+
+**No test example has the exact same prompt text as any training example.**
+
+### 2. Template + Scenario Pairs: 0% overlap
+
+| Split | Template+Scenario Pair Overlap |
+|---|---|
+| test_in_distribution | 0 / 1,455 (0.0%) |
+| test_template_ood | 0 / 3,503 (0.0%) |
+| test_use_case_ood | 0 / 4,341 (0.0%) |
+| test_sector_ood | 0 / 4,579 (0.0%) |
+| test_adversarial | 0 / 33 (0.0%) |
+
+**No test example combines the same template AND scenario as training.** Even when templates overlap, the scenarios are always different.
+
+### 3. OOD Split Construction Validation
+
+| Split | OOD Criterion | OOD % | In-Train % |
+|---|---|---|---|
+| test_template_ood | prompt_template_id | **100.0%** (65/65) | 0.0% |
+| test_use_case_ood | scenario_id | **100.0%** (2545/2545) | 0.0% |
+| test_sector_ood | scenario_id | **100.0%** (2769/2769) | 0.0% |
+| test_adversarial | prompt_template_id | **100.0%** (33/33) | 0.0% |
+| test_in_distribution | — | 40.3% OOD scenarios | 59.7% ID |
+
+### 4. Completion Overlap Analysis
+
+| Split | Completion Overlap | Explanation |
+|---|---|---|
+| test_in_distribution | 60.3% | Original random split; some deterministic lifecycle outputs |
+| test_template_ood | 44.6% | Same reason, fewer lifecycle ops in this split |
+| **test_use_case_ood** | **0.0%** | **No identical completions — genuinely OOD** |
+| **test_sector_ood** | **0.0%** | **No identical completions — genuinely OOD** |
+| test_adversarial | 100.0% | Expected — standardized rejection responses |
+
+### 5. What "High Char-Ngram Similarity" Actually Means
+
+The journal reports:
+- ≥90% similarity: 1,290 / 2,521
+- ≥95% similarity: 602 / 2,521
+- ≥98% similarity: 262 / 2,521
+
+**This measures structural similarity, not content duplication.**
+
+All prompts follow templated patterns:
+- *"Set up a network slice for [use_case] at [region]"*
+- *"Deploy a [slice_type] slice for [use_case] with [latency] ms latency"*
+
+The `prompt_normalized` column confirms this — it replaces variables with placeholders like `<use_case>`, `<region>`, `<num>`.
+
+**High char-ngram similarity = same sentence structure with different values = EXPECTED and CORRECT for a templated dataset.**
+
+## Conclusion
+
+**There is NO data leakage.** The OOD splits are scientifically valid:
+
+1. `test_template_ood` uses **100% held-out templates**
+2. `test_use_case_ood` uses **100% held-out scenarios** (use cases)
+3. `test_sector_ood` uses **100% held-out scenarios** (sectors)
+4. Zero exact prompt duplication
+5. Zero template+scenario pair duplication
+6. Zero completion overlap for use-case and sector OOD splits
+
+## Recommendation for Paper
+
+Add the following to the methodology section:
+
+> "While char-ngram similarity between train and test prompts appears high due to shared templated sentence structures, we confirm zero exact prompt duplication and zero template+scenario pair overlap. The OOD splits are constructed by holding out distinct prompt templates (test_template_ood), use cases (test_use_case_ood), and sectors (test_sector_ood), with 100% of held-out examples using scenarios not present in training."
+
+## Scripts Used
+
+This analysis was performed with automated scripts that:
+1. Loaded all splits from the dataset
+2. Computed exact text overlap for prompts, completions, and normalized prompts
+3. Checked template_id, scenario_id, and json_structure_id overlap
+4. Verified template+scenario pair uniqueness
+5. Analyzed completion overlap by target_layer
+
+All computations are reproducible from the published dataset.