Enterprise AI Analysis
HalluGraph: Auditable Hallucination Detection for Legal RAG Systems via Knowledge Graph Alignment
Legal AI systems powered by retrieval-augmented generation (RAG) face a critical accountability challenge: when an AI assistant cites case law, statutes, or contractual clauses, practitioners need verifiable guarantees that generated text faithfully represents source documents. Existing hallucination detectors rely on semantic similarity metrics that tolerate entity substitutions, a dangerous failure mode when confusing parties, dates, or legal provisions can have material consequences. We introduce HalluGraph, a graph-theoretic framework that quantifies hallucinations through structural alignment between knowledge graphs extracted from context, query, and response. Our approach produces bounded, interpretable metrics decomposed into Entity Grounding (EG), measuring whether entities in the response appear in source documents, and Relation Preservation (RP), verifying that asserted relationships are supported by context.
Executive Impact: Key Findings for Your Enterprise
The increasing deployment of AI in legal practice demands rigorous verification. HalluGraph addresses the critical challenge of AI hallucination in legal Retrieval-Augmented Generation (RAG) systems by providing auditable and interpretable metrics. Unlike traditional semantic similarity methods, HalluGraph uses knowledge graph alignment to precisely quantify whether entities and relationships in an AI's response are faithfully represented in source documents. This prevents dangerous errors like misattributed holdings or fabricated citations, which can have material legal consequences. Our framework offers Entity Grounding (EG), Relation Preservation (RP), and a Composite Fidelity Index (CFI), providing clear, traceable insights into the AI's fidelity. It consistently outperforms semantic baselines, making it an essential guardrail for high-stakes legal applications.
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Deep Analysis & Enterprise Applications
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HalluGraph constructs knowledge graphs (Gc, Gq, Ga) from legal documents, queries, and responses. Each graph consists of entity nodes (persons, organizations, dates, legal provisions) and directed edges representing relations. Entity extraction uses spaCy NER with legal extensions, while relation extraction employs an instruction-tuned SLM to output (subject, relation, object) triples in JSON format, following OpenIE conventions.
Four bounded metrics in [0, 1] are defined: Entity Grounding (EG) measures the fraction of response entities appearing in source documents, capturing entity substitution hallucinations. Relation Preservation (RP) verifies that asserted relationships are supported by context, capturing structural hallucinations. The Composite Fidelity Index (CFI) aggregates EG and RP with learned weights. When the response graph is subgraph-isomorphic to the source graph, EG and RP are provably 1.
HalluGraph effectively detects hallucinations on high-stakes legal generation tasks, consistently outperforming semantic similarity (BERTScore) and NLI baselines. For instance, on Legal Contract QA, HalluGraph achieves an AUC of 0.94, far surpassing BERTScore's 0.60. Similarly, on Legal Case QA, it achieves 0.84. On synthetic control tasks, it approaches perfect discrimination (AUC ≥ 0.99).
HalluGraph's performance improves with context length and entity density. It operates effectively in a 'high-context regime' (>400 words, >20 entities), typical of legal documents like contracts and case opinions, where it achieves robust discrimination (AUC ≈ 0.89). In short-context scenarios (<10 entities), performance drops below chance due to sparse graph structures.
HalluGraph plugs directly into legal Retrieval-Augmented Generation (RAG) pipelines as a post-generation guardrail. Responses meeting composite fidelity thresholds are passed through, while low-scoring responses trigger re-retrieval or human review. This ensures verifiable accountability in legal AI deployment.
Unlike black-box similarity scores, every HalluGraph flag is accompanied by a concrete explanation (e.g., 'missing entity: case name not in context' or 'unsupported edge: holding not supported'). This yields a fine-grained audit trail that can be surfaced to human reviewers and regulators, providing a clear path to remediation.
0.979
AUC for HalluGraph on structured control documents
HalluGraph Processing Pipeline
Legal Document (Gc)
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Query (Gq)
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Triple Extraction (SLM)
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Response (Ga)
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Compute Alignment Metrics (EG, RP, CFI)
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Decision & Audit Trail
| Dataset | HalluGraph | NLI | BERTScore |
|---|---|---|---|
| Legal Contract QA | 0.94 | 0.92 | 0.60 |
| Legal Case QA | 0.84 | 0.69 | 0.54 |
| Coral Biology | 1.00 | 0.72 | 0.59 |
| Economics | 0.99 | 0.68 | 0.55 |
| HalluGraph consistently outperforms semantic similarity and NLI baselines, demonstrating superior hallucination detection for legal RAG tasks. | |||
HalluGraph in Legal RAG Guardrails
HalluGraph is designed to seamlessly integrate into legal Retrieval-Augmented Generation (RAG) pipelines as a post-generation guardrail. This ensures that AI-generated legal content is fully auditable and trustworthy.
Strict Citation Checks
For case law research, Entity Grounding verifies parties, reporter citation, and year appear in retrieved documents. Relation Preservation checks that attributed holdings are supported by the source graph, preventing hallucinations from unrelated precedent.
Contractual Fidelity
In contract review and clause extraction, EG ensures referenced parties, amounts, and provisions exist in the source contract. RP verifies that asserted obligations preserve the relational structure, guarding against subtle assignment errors (e.g., swapping Tenant and Landlord) that are often invisible to similarity-based metrics.
This granular verification, with concrete explanations for every flagged hallucination, provides the transparency and accountability required for high-stakes legal applications, enabling practitioners to deploy LLM assistants with verifiable guarantees.
Calculate Your Potential ROI with Auditable AI
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Your Enterprise AI Implementation Roadmap
Our structured approach ensures a seamless transition and maximum ROI for your organization.
Phase 1: Initial Integration & Pilot
Deploy HalluGraph within a controlled RAG environment for internal legal document review. Establish baseline performance and gather user feedback.
Phase 2: Expanded Scope & Customization
Extend HalluGraph to support diverse legal workflows (e.g., litigation support, compliance). Develop custom entity/relation types for specialized domains.
Phase 3: Real-time Monitoring & Feedback Loop
Implement real-time hallucination monitoring. Integrate audit trails into existing legal tech platforms for seamless practitioner review and continuous model improvement.
Phase 4: Regulatory Compliance & Certification
Work towards independent certification for HalluGraph's auditing capabilities, aligning with evolving legal AI regulations for broader public-sector adoption.
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