To extend and partially replace my earlier DSPy.GEPA-based implementation (available at https://www.jrdatahub.com/gepa-based-sbert), I developed a new pipeline using GEPA’s optimize_anything module to refine legal category descriptions for classifying state-level court rulings. While the previous implementation focused on DSPy.GEPA, this version adopts optimize_anything early in the workflow to provide a more automated and generalized optimization framework.
The workflow uses SBERT embeddings to compare documents (chunked for long texts) to predefined category paragraphs representing four constitutional education categories: equal protection, education adequacy, both, and other. In addition to paragraph refinement, this implementation incorporates alpha tuning when aggregating similarity scores (e.g., combining mean and max cosine similarity across chunks). The alpha parameter is optimized to balance document-level semantic consistency and high-salience chunk matching, further improving classification robustness.
optimize_anything iteratively proposes candidate refinements, which are evaluated using a macro-F1 metric computed from SBERT similarity scores between document embeddings and paragraph embeddings, incorporating the tuned alpha weighting. The optimizer selects candidates that maximize macro-F1 across the training set. Refined paragraphs are saved as JSON/CSV files for reproducibility, and SBERT similarity scores are recomputed to classify documents based on maximum similarity. Final performance metrics, including precision, recall, and F1-score, are computed for each split to assess improvements over baseline.
In this run, the refined paragraphs remained nearly identical to the original seeds. This outcome is expected and reflects several factors:
-
Initial paragraphs were already strong, clearly capturing distinctions between categories.
-
SBERT embeddings are robust, so minor textual changes often do not materially affect similarity scores.
-
Macro-F1 rewards only changes that affect predicted labels, so refinements that do not alter document classification are not reinforced.
-
Alpha tuning already optimized similarity aggregation, reducing the marginal benefit of additional textual adjustments.
Consequently, optimize_anything effectively verifies and confirms high-quality category descriptions without introducing unnecessary changes, ensuring both semantic clarity and interpretability. While this implementation replaces the earlier DSPy.GEPA-based refinement framework, differences in optimization structure, scoring aggregation, and automation level may produce variations in other settings. Such variation is methodologically acceptable and reflects differences in optimization design rather than inconsistency in results.
#GEPA #optimize_anything #legal_text_analysis