Beyond Retrieval: Measuring the Value of Explicit Knowledge Grounding in Legal AI

Source abstract

Large language models can produce convincing answers to questions about legal documents, but linguistic plausibility does not establish whether those answers preserve the meaning of the source or whether their claims can be traced to supporting evidence. Retrieval-based approaches reduce part of this uncertainty by providing external textual context, yet retrieved passages do not necessarily make explicit the legal entities, relations, conditions, exceptions, and provenance that determine why the evidence supports a particular conclusion. This study introduces Knowledge-grounded Legal AI (KGLAI), an architecture that combines Legal NLP, a domain ontology, a document-grounded knowledge graph, provenance, semantic validation, and language generation within a common evidence flow. KGLAI was evaluated against a textually grounded LLM in a controlled paired experiment involving seven non-disclosure agreements, 70 document-question cases, and 420 model executions. Both treatments used the same documents, questions, language model, and generation configuration. Legal fidelity was evaluated through the Legal Fidelity Index (LFI), documentary traceability through STI-D, and the additional semantic evidence path available in KGLAI through STI-S. KGLAI achieved a mean LFI of 0.8029 compared with 0.7050 for textual grounding and increased documentary traceability from 0.6498 to 0.7536. The difference in legal fidelity became larger as question complexity increased, from 0.0348 for direct extraction tasks to 0.1715 for questions requiring evidence integration across contractual provisions. KGLAI also preserved a mean semantic traceability score of 0.8811, while evidence coverage increased and unsupported claims decreased. These findings indicate that the contribution of explicit knowledge grounding is most visible when legal questions depend on relations, conditions, exceptions, or evidence distributed across the document. Rather than replacing legal judgment, the proposed architecture provides an experimentally testable mechanism for making the evidence behind generated legal answers more explicit and inspectable.