GAVEL — Graph-Augmented Paragraph Retrieval for Legal Judgments

Extended LexCLiPR to address a limitation of fine-grained legal retrieval: chunking judgments into paragraphs strips the document context that determines relevance.

Modeled each judgment as its own paragraph-level graph, training dual 3-layer Graph Attention Networks — local 5-hop adjacency and global BERTopic topic nodes — under contrastive loss.

Outperformed a fully fine-tuned bi-encoder using a frozen mDPR encoder: Recall@5% of 53.5 vs 44.4 on seen queries and 39.0 vs 30.5 on unseen, against a 22.6 zero-shot baseline.

Showed combining local and global views beats either alone (53.5 vs 51.2 / 52.2), while per-layer fusion collapses to 35.2 through over-smoothing.