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results/baselines/qwen3_8b_zero_shot_normalized_200.json
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|
| 1 |
+
{
|
| 2 |
+
"test_adversarial": {
|
| 3 |
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|
| 4 |
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"parse_json": 0.0,
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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"by_target_layer": {
|
| 26 |
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|
| 27 |
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"num_examples": 17,
|
| 28 |
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"parse_json": 0.0,
|
| 29 |
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"gold_parse_json": 1.0,
|
| 30 |
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"exact_match": 0.0,
|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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},
|
| 50 |
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"adversarial_contradictory": {
|
| 51 |
+
"num_examples": 9,
|
| 52 |
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"parse_json": 0.0,
|
| 53 |
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"gold_parse_json": 1.0,
|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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"parse_json": 0.0,
|
| 77 |
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|
| 78 |
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"exact_match": 0.0,
|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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}
|
| 98 |
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},
|
| 99 |
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"by_slice_type": {
|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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| 112 |
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| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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}
|
| 124 |
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},
|
| 125 |
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"by_lifecycle_operation": {
|
| 126 |
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"create": {
|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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| 132 |
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| 133 |
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| 134 |
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"norm_field_precision": 0.0,
|
| 2370 |
+
"norm_field_recall": 0.0,
|
| 2371 |
+
"norm_field_f1": 0.0,
|
| 2372 |
+
"norm_key_precision": 0.0,
|
| 2373 |
+
"norm_key_recall": 0.0,
|
| 2374 |
+
"norm_key_f1": 0.0
|
| 2375 |
+
}
|
| 2376 |
+
}
|
| 2377 |
+
}
|
| 2378 |
+
}
|
results/baselines/qwen3_8b_zero_shot_raw_200.json
ADDED
|
@@ -0,0 +1,1412 @@
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|
| 1 |
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}
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| 1315 |
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},
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| 1317 |
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| 1318 |
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| 1332 |
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| 1347 |
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| 1360 |
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},
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| 1377 |
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| 1379 |
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| 1380 |
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| 1393 |
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| 1394 |
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| 1395 |
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| 1396 |
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|
| 1397 |
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| 1398 |
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| 1399 |
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| 1401 |
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| 1402 |
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| 1407 |
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|
| 1408 |
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|
| 1409 |
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}
|
| 1410 |
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}
|
| 1411 |
+
}
|
| 1412 |
+
}
|
results/baselines/zero_shot_vs_finetuned.md
ADDED
|
@@ -0,0 +1,13 @@
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|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
| 1 |
+
# Zero-shot Qwen3-8B vs Fine-tuned Qwen3-8B QLoRA
|
| 2 |
+
|
| 3 |
+
Zero-shot baseline was evaluated on 200 examples per split. Fine-tuned results are full split metrics.
|
| 4 |
+
|
| 5 |
+
| Split | Zero-shot parse | Fine-tuned parse | Zero-shot norm field F1 | Fine-tuned norm field F1 | Zero-shot norm key F1 | Fine-tuned norm key F1 |
|
| 6 |
+
|---|---:|---:|---:|---:|---:|---:|
|
| 7 |
+
| ID | 0.335 | 1.000 | 0.0009 | 0.7956 | 0.0169 | 0.9811 |
|
| 8 |
+
| Template OOD | 0.340 | 1.000 | 0.0014 | 0.7865 | 0.0172 | 0.9801 |
|
| 9 |
+
| Use-case OOD | 0.325 | 0.9998 | 0.0012 | 0.7907 | 0.0198 | 0.9805 |
|
| 10 |
+
| Sector OOD | 0.345 | 1.000 | 0.0008 | 0.7697 | 0.0171 | 0.9818 |
|
| 11 |
+
| Adversarial | 0.000 | 1.000 | 0.0000 | 0.9697 | 0.0000 | 1.0000 |
|
| 12 |
+
|
| 13 |
+
Conclusion: domain QLoRA fine-tuning is essential for structured telecom intent-to-config generation.
|