Specificity F1 Improvement Plan

Goal: Macro F1 > 0.80 on both category and specificity heads Current: Cat F1=0.932 (passing), Spec F1=0.517 (needs ~+0.28) Constraint: Specificity is paragraph-level and category-independent by design

Diagnosis

Per-class spec F1 (best run, epoch 5):

L1 (Generic): ~0.79
L2 (Domain-Adapted): ~0.29
L3 (Firm-Specific): ~0.31
L4 (Quantified): ~0.55

L2 and L3 drag macro F1 from ~0.67 average to 0.52. QWK=0.840 shows ordinal ranking is strong — the problem is exact boundary placement between adjacent levels.

Root causes

CORAL's shared weight vector. CORAL uses logit_k = w·x + b_k — one weight vector for all thresholds. But the three transitions require different features:
- L1→L2: cybersecurity terminology detection (ERM test)
- L2→L3: firm-unique fact detection (named roles, systems)
- L3→L4: quantified/verifiable claim detection (numbers, dates) A single w can't capture all three signal types.
[CLS] pooling loses distributed signals. A single "CISO" mention anywhere in a paragraph should bump to L3, but [CLS] may not attend to it.
Label noise at boundaries. 8.7% of training labels had Grok specificity disagreement, concentrated at L1/L2 and L2/L3 boundaries.
Insufficient training. Model was still improving at epoch 5 — not converged.

Ideas (ordered by estimated ROI)

Tier 1 — Implement first

A. Independent threshold heads (replace CORAL) Replace the single CORAL weight vector with 3 independent binary classifiers, each with its own learned features:

threshold_L2plus: Linear(hidden, 1) — "has any qualifying facts?"
threshold_L3plus: Linear(hidden, 1) — "has firm-specific facts?"
threshold_L4: Linear(hidden, 1) — "has quantified/verifiable facts?"

Same cumulative binary targets as CORAL, but each threshold has independent weights. Optionally upgrade to 2-layer MLP (hidden→256→1) for richer decision boundaries.

B. High-confidence label filtering Only train specificity on paragraphs where all 3 Grok runs agreed on specificity level (~91.3% of data, ~59K of 65K). The ~6K disagreement cases are exactly the noisy boundary labels that confuse the model. Category labels can still use all data.

C. More epochs + early stopping on spec F1 Run 15-20 epochs. Switch model selection metric from combined_macro_f1 to spec_macro_f1 (since category already exceeds 0.80). Use patience=5.

D. Attention pooling Replace [CLS] token pooling with learned attention pooling over all tokens. This lets the model attend to specific evidence tokens (CISO, $2M, NIST) distributed anywhere in the paragraph.

Tier 2 — If Tier 1 insufficient

E. Ordinal consistency regularization Add a penalty when threshold k fires but threshold k-1 doesn't (e.g., model says "has firm-specific" but not "has domain terms"). Weight ~0.1.

F. Differential learning rates Backbone: 1e-5, heads: 5e-4. Let the heads learn classification faster while the backbone makes only fine adjustments.

G. Softmax head comparison Try standard 4-class CE (no ordinal constraint at all). If it outperforms both CORAL and independent thresholds, the ordinal structure isn't helping.

H. Multi-sample dropout Apply N different dropout masks, average logits. Reduces variance in the specificity head's predictions, especially for boundary cases.

Tier 3 — If nothing else works

I. Specificity-focused auxiliary task The consensus labels include specific_facts arrays with classified fact types (domain_term, named_role, quantified, etc.). Add a token-level auxiliary task that detects these fact types. Specificity becomes "what's the highest-level fact type present?" — making the ordinal structure explicit.

J. Separate specificity model Train a dedicated model just for specificity with a larger head, more specificity-focused features, or a different architecture (e.g., token-level fact extraction → aggregation).

K. Re-annotate boundary cases Use GPT-5.4 to re-judge the ~9,323 majority-vote cases where Grok had specificity disagreement. Cleaner labels at boundaries.

Experiment Log

Experiment 1: Independent thresholds + attention pooling + MLP + filtering (15 epochs)

Config: configs/finetune/iter1-independent.yaml

Specificity head: independent (3 separate Linear(1024→256→1) binary classifiers)
Pooling: attention (learned attention over all tokens)
Confidence filtering: only train spec on unanimous labels
Ordinal consistency regularization: 0.1
Class weighting: yes
Base checkpoint: ModernBERT-large (no DAPT/TAPT)
Epochs: 15

Results:

Epoch	Combined	Cat F1	Spec F1	QWK
1	0.855	0.867	0.844	0.874
2	0.913	0.909	0.918	0.935
3	0.925	0.919	0.931	0.945
4	0.936	0.932	0.940	0.950
5	0.938	0.936	0.940	0.949
8	0.944	0.943	0.945	0.952
10	0.944	0.943	0.945	0.952
11	0.944	0.945	0.944	0.952

Stopped at epoch 11 — train-eval loss gap was 8× (0.06 vs 0.49) with no further eval F1 improvement. Best checkpoint: epoch 8 (spec F1=0.945).

Conclusion: Massive improvement — spec F1 went from 0.517 (CORAL baseline) to 0.945 at epoch 8. Both targets (>0.80 cat and spec F1) exceeded by epoch 1. Independent thresholds were the key insight — CORAL's shared weight vector was the primary bottleneck. Attention pooling, MLP heads, and confidence filtering all contributed. Tier 2 and Tier 3 ideas were not needed.

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