Definition
Attribution in AI systems refers to the process of establishing explicit connections between generated claims and their supporting evidence. While citation adds references to responses, attribution goes deeper—it determines whether a specific claim is actually entailed by (logically supported by) the cited source. A well-attributed AI system can answer “What evidence supports this statement?” for every factual claim it makes. Attribution is crucial for trustworthy AI because it enables verification, prevents the illusion of grounding (citing sources that don’t actually support claims), and makes AI decision-support systems auditable.
Why it matters
Attribution is essential for accountable AI:
- Prevents false grounding — ensures cited sources actually support claims
- Enables auditing — every claim traceable to specific evidence
- Supports compliance — regulatory requirements for decision transparency
- Builds trust — users can verify the reasoning chain
- Catches hallucinations — unattributed claims are flagged
- Improves reliability — forces model to stick to what sources say
Citation without attribution is decoration. Attribution without citation is hidden. Together, they create verifiable AI.
How it works
┌────────────────────────────────────────────────────────────┐
│ ATTRIBUTION │
├────────────────────────────────────────────────────────────┤
│ │
│ CITATION vs ATTRIBUTION: │
│ ──────────────────────── │
│ │
│ CITATION ONLY (can be superficial): │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ │ │
│ │ Response: "The project deadline is Jan 15 [1]" │ │
│ │ Source [1]: "Q4 planning should be done by Dec" │ │
│ │ │ │
│ │ ⚠️ Citation exists but doesn't support claim! │ │
│ │ The source says nothing about Jan 15 deadline. │ │
│ │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ PROPER ATTRIBUTION: │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ │ │
│ │ Response: "The project deadline is Jan 15 [1]" │ │
│ │ Source [1]: "Final deliverables due January 15" │ │
│ │ │ │
│ │ ✓ Attribution verified: source entails claim │ │
│ │ The source explicitly states the deadline. │ │
│ │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ │
│ ATTRIBUTION CLASSIFICATION: │
│ ─────────────────────────── │
│ │
│ For each claim + cited source pair: │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ │ │
│ │ ATTRIBUTABLE (source supports claim): │ │
│ │ ├── Exact match: claim uses same words as source │ │
│ │ ├── Paraphrase: claim restates source meaning │ │
│ │ └── Inference: claim logically follows from src │ │
│ │ │ │
│ │ NOT ATTRIBUTABLE (source doesn't support): │ │
│ │ ├── Irrelevant: source discusses different topic │ │
│ │ ├── Contradicted: source says opposite │ │
│ │ ├── Extrapolated: claim goes beyond source │ │
│ │ └── Fabricated: no source relationship at all │ │
│ │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ │
│ ATTRIBUTION PIPELINE: │
│ ───────────────────── │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ │ │
│ │ 1. CLAIM EXTRACTION │ │
│ │ ┌─────────────────────────────────────────────┐ │ │
│ │ │ Response: "Company revenue grew 15% [1] and │ │ │
│ │ │ market share increased to 23% [2] in Q4." │ │ │
│ │ │ │ │ │
│ │ │ Extracted claims: │ │ │
│ │ │ • Claim A: "Revenue grew 15%" [cites 1] │ │ │
│ │ │ • Claim B: "Market share is 23%" [cites 2] │ │ │
│ │ │ • Claim C: "This happened in Q4" [cites ?] │ │ │
│ │ └─────────────────────────────────────────────┘ │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ 2. SOURCE RETRIEVAL │ │
│ │ ┌─────────────────────────────────────────────┐ │ │
│ │ │ Source [1]: "FY24 saw revenue growth of │ │ │
│ │ │ 15.2% compared to prior year" │ │ │
│ │ │ │ │ │
│ │ │ Source [2]: "Market share reached 22.8% │ │ │
│ │ │ as of end of fiscal year" │ │ │
│ │ └─────────────────────────────────────────────┘ │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ 3. ENTAILMENT VERIFICATION │ │
│ │ ┌─────────────────────────────────────────────┐ │ │
│ │ │ │ │ │
│ │ │ Claim A: "Revenue grew 15%" │ │ │
│ │ │ Source 1: "revenue growth of 15.2%" │ │ │
│ │ │ → ATTRIBUTABLE ✓ (15% ≈ 15.2%) │ │ │
│ │ │ │ │ │
│ │ │ Claim B: "Market share is 23%" │ │ │
│ │ │ Source 2: "Market share reached 22.8%" │ │ │
│ │ │ → ATTRIBUTABLE ✓ (23% ≈ 22.8%) │ │ │
│ │ │ │ │ │
│ │ │ Claim C: "This happened in Q4" │ │ │
│ │ │ Sources: mention "FY" and "fiscal year" │ │ │
│ │ │ → NOT ATTRIBUTABLE ⚠️ (Q4 not mentioned) │ │ │
│ │ │ │ │ │
│ │ └─────────────────────────────────────────────┘ │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ 4. ATTRIBUTION SCORING │ │
│ │ ┌─────────────────────────────────────────────┐ │ │
│ │ │ │ │ │
│ │ │ Response attribution score: │ │ │
│ │ │ • 2 of 3 claims properly attributed │ │ │
│ │ │ • Score: 66.7% │ │ │
│ │ │ │ │ │
│ │ │ Actions: │ │ │
│ │ │ • Flag Claim C for review │ │ │
│ │ │ • Or: Remove/rephrase Claim C │ │ │
│ │ │ • Or: Find supporting source for Q4 │ │ │
│ │ │ │ │ │
│ │ └─────────────────────────────────────────────┘ │ │
│ │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ │
│ ATTRIBUTION METHODS: │
│ ──────────────────── │
│ │
│ NLI-based (Natural Language Inference): │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Use NLI model to classify: │ │
│ │ Premise: [source text] │ │
│ │ Hypothesis: [claim] │ │
│ │ │ │
│ │ → Entailment: Source supports claim ✓ │ │
│ │ → Neutral: Source doesn't address claim │ │
│ │ → Contradiction: Source contradicts claim ✗ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ LLM-based verification: │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Prompt LLM: │ │
│ │ "Does the following source support this claim? │ │
│ │ │ │
│ │ Source: [source text] │ │
│ │ Claim: [claim text] │ │
│ │ │ │
│ │ Answer: Supported / Not Supported / Contradicted │ │
│ │ Evidence: [quote from source]" │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ Embedding similarity: │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Compare semantic similarity between claim and │ │
│ │ source embeddings. High similarity suggests │ │
│ │ potential attribution (needs verification). │ │
│ │ │ │
│ │ Note: Similarity ≠ entailment │ │
│ │ "The sky is blue" and "The sky is not blue" │ │
│ │ are semantically similar but contradictory! │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ │
│ ATTRIBUTION METRICS: │
│ ──────────────────── │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Metric │ Formula │ │
│ │ ────────────────┼──────────────────────────────── │ │
│ │ Attribution │ # attributed claims │ │
│ │ precision │ ───────────────────── │ │
│ │ │ # claims with citations │ │
│ │ │ │ │
│ │ Attribution │ # attributed claims │ │
│ │ recall │ ───────────────────── │ │
│ │ │ # total factual claims │ │
│ │ │ │ │
│ │ AIS (Attrib. │ Human eval of whether sources │ │
│ │ to Identified │ fully support generated text │ │
│ │ Sources) │ │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ │
│ COMMON ATTRIBUTION FAILURES: │
│ ──────────────────────────── │
│ │
│ • Citing topically related but non-supporting sources │
│ • Over-generalizing from specific source claims │
│ • Combining multiple sources incorrectly │
│ • Citing source that contradicts claim │
│ • Citing source for wrong part of claim │
│ │
└────────────────────────────────────────────────────────────┘Common questions
Q: How is attribution different from citation?
A: Citation adds references; attribution verifies them. A response can have perfect citations (every claim has [1], [2], [3]) but poor attribution (the cited sources don’t actually support the claims). Attribution is the quality check on citations.
Q: How do I implement attribution verification?
A: Common approaches: (1) NLI models that classify premise-hypothesis pairs, (2) LLM-as-judge prompting to verify source-claim support, (3) Human evaluation for high-stakes applications. RAGAS and similar frameworks provide attribution metrics.
Q: Should every claim be attributed?
A: Factual claims requiring evidence should be attributed. Common knowledge (“Paris is in France”), logical inferences clearly derived from attributed facts, and opinions/analysis don’t require attribution. Focus on claims users might dispute or verify.
Q: What’s an acceptable attribution score?
A: Depends on the application. High-stakes domains (legal, medical, financial) should target 95%+. General knowledge assistants might accept 80-90%. The key is transparency—users should know the system’s attribution reliability.
Related terms
- Citation — adding source references
- Grounding — anchoring to sources
- Factuality — accuracy of claims
- RAG — retrieval that enables attribution
References
Rashkin et al. (2023), “Measuring Attribution in Natural Language Generation Models”, ACL. [AIS metric and attribution evaluation]
Bohnet et al. (2022), “Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models”, arXiv. [Attributed QA framework]
Yue et al. (2023), “Automatic Evaluation of Attribution by Large Language Models”, EMNLP. [LLM-based attribution evaluation]
Gao et al. (2023), “RARR: Researching and Revising What Language Models Say”, ACL. [Attribution-based revision]