TWBG_Materials/文档润色流和知识库构建流/claude-scholar-upstream/skills/results-analysis/examples/example-analysis-report.md

Strict Analysis Report

Analysis Question

• Compare Full Model, Frozen Encoder, and Subject Adapter on cross-subject decoding.
• Determine whether the adapter closes the transfer gap without sacrificing stability.

Data Inventory

• 3 model families
• 5 random seeds per family
• subject-level WER and CER
• training logs for convergence
• ablation outputs for adapter width

Executive Summary

• Subject Adapter improves mean WER over Frozen Encoder by 3.8 absolute points.
• The improvement is statistically significant under paired testing after multiple-comparison correction.
• Full fine-tuning remains strongest overall, but Subject Adapter offers the best compute-performance tradeoff.
• Performance variance is lower than Frozen Encoder, suggesting a more stable transfer path.

Main Findings

1. Main comparison

• Full fine-tuning: 31.4 ± 1.9 WER
• Subject Adapter: 33.2 ± 1.3 WER
• Frozen Encoder: 37.0 ± 2.1 WER

Observation:

• Subject Adapter consistently outperforms Frozen Encoder across all 5 seeds.

Interpretation:

• The transfer bottleneck is not purely feature reuse; a light adaptation head captures subject-specific alignment that freezing alone misses.

Implication:

• Future work should prioritize adapter variants before investing in heavier full-model tuning.

2. Stability

• Subject Adapter has the smallest seed variance among transfer-friendly methods.
• Convergence curves show less oscillation after epoch 8.

Interpretation:

• Adapter tuning is not only better on average, but easier to optimize.

3. Ablation

• Reducing adapter width from 256 to 64 hurts WER by 1.6 points.
• Increasing from 256 to 512 yields only marginal improvement.

Interpretation:

• Most of the benefit is captured at moderate width; scale-up is not the current bottleneck.

Caveats

• Only 5 seeds; inference about tails remains limited.
• Subject count is modest, so subject-level heterogeneity may still be under-estimated.
• Training-time comparison depends on identical hardware assumptions.

Recommended Next Move

• Promote this finding into a results-report note focused on adapter vs freezing.
• Run one robustness pass on held-out low-resource subjects.