Wrong Source Cited

Issue: Citation Points to Different Document Than Information Came From

Frequency: Common

Symptoms

• Citation exists but document doesn’t contain the claim
• Source attribution confused between similar documents
• User clicks citation, finds different content
• Multiple sources retrieved, wrong one cited

Root Cause When multiple documents are in context, model may confuse which source contained which information, especially with similar documents.

Example

Retrieved documents:
[1] Q2 Report: "Revenue increased 12% year-over-year"
[2] Q3 Report: "Revenue increased 18% year-over-year"

Agent response: "Revenue increased 18% year-over-year [1]"

Error: 18% is from Q3 Report [2], not Q2 Report [1]

Result: User references Q2 report but finds 12%, not 18%

Mitigation Strategies

1. Extractive citations: Quote the exact text, attribute to source
2. Source-claim verification: Validate each citation before output
3. Inline source marking: Mark source while extracting, not after
4. Single-source answers: Answer from one document when possible
5. Citation validation step: Post-process to verify attribution
6. Distinct source identifiers: Make sources clearly distinguishable

Detection

• Automated citation verification
• Compare cited text to source content
• Track citation accuracy by source count
• User reports of wrong citations

Mitigation Strategies

Prevention

1. Implement query-answer consistency validation: Decompose complex queries into atomic components and verify each component is addressed in the answer before returning. Use RAGAS Answer Relevancy metric (target: >0.75) with automatic re-generation for scores below threshold. Root cause mitigation: Prevents context-anchoring by explicitly binding answer generation to parsed query intent.

2. Apply multi-source consensus verification: Require answers synthesized from multiple sources to explicitly cite which sources support each claim and flag unresolved contradictions. Implementation: Use semantic similarity checks across source fragments to detect cherry-picked evidence patterns. Root cause: Ensures balanced representation of evidence when sources conflict.

3. Enforce comprehensive coverage checks: Implement structured extraction requiring explicit treatment of caveats, limitations, exceptions, and counterevidence for each claim type. Use template responses with mandatory caveat sections. Root cause: Prevents omission of qualifying information that would change user decision-making.

Detection & Response

1. Answer completeness monitoring: Measure coverage of query intents in generated answers. Track query decomposition rate (% of query components explicitly addressed) and flag responses with coverage <85%. Instrument RAG pipeline to log query-answer similarity scores per component. Alert on sustained scores <0.70.

2. Evidence balance scoring: For each answer, compute evidence distribution across sources and flag one-sided responses (>70% from single source on multi-source queries). Implement automated extraction of caveat/limitation mentions and track inclusion rates by query type. Target: >80% of medical/financial answers include relevant caveats.

Architecture Patterns

1. Query Intent Decomposition Graph: Parse complex queries into a DAG of atomic intents before retrieval. Each retrieved document is mapped to specific intent nodes. Answer generation must satisfy all leaf nodes. Validation layer computes coverage before response generation.

2. Evidence Consensus Engine: Maintain a fact graph where each claim is attributed to specific sources with confidence scores. Multi-source claims require consensus computation (intersection of sources supporting claim). Flagging layer surfaces contradictions to generation model.

3. Structured Response Templates: Use task-specific response schemas that enforce inclusion of: primary answer, supporting evidence, relevant caveats/exceptions, alternative interpretations, confidence bounds. Auto-flag template violations before user delivery.

Key Metrics

Alerts & Escalation

References

• Stanford Legal RAG Hallucinations - Citation accuracy issues
• Mindee: RAG Hallucinations Explained - Source attribution errors