Context Adherence

The core principle of context adherence is that every factual claim in a response must be entailed by the provided source text. The model is prohibited from introducing parametric knowledge—facts learned during pre-training—if they are not explicitly stated in the context. This is enforced through Natural Language Inference (NLI) entailment checks, where a verifier model classifies each generated statement as either 'entailed by,' 'contradicting,' or 'neutral to' the source. A response with perfect adherence has zero unsupported claims.

A context-adherent model must be calibrated to abstain from answering when the provided context is insufficient. Instead of guessing or pulling from general knowledge, the model should output an explicit 'insufficient information' signal. This behavior is trained using datasets that include unanswerable queries paired with relevant but incomplete contexts. The key metric here is the model's precision in distinguishing between questions that are answerable from the context and those that are not, minimizing speculative fabrications.

Advanced context adherence requires fine-grained attribution scoring, where each generated sentence is mapped back to a specific span of text in the source document. This creates a verifiable provenance chain. Techniques involve:

• Post-hoc attribution: Using gradient-based methods to highlight which input tokens most influenced an output token.
• Inline citation: Training the model to generate a citation marker immediately after a factual claim, linking it to a source paragraph.

A robust context-adherent model must resist contextual distractions. This means the model should not be swayed by irrelevant information inserted into the context (a 'needle in a haystack' test) or by normative conflicts where the context contradicts the model's internal training data. The model must consistently privilege the user-provided context over its own weights, a behavior reinforced through Direct Preference Optimization (DPO) on pairs of responses—one that follows the context faithfully and one that defaults to parametric knowledge.

Context adherence is measured using automated metrics that compare the generated response (hypothesis) against the source context (premise):

• Entailment Ratio: The percentage of generated sentences classified as entailed by an NLI model.
• Hallucination Rate: The percentage of sentences classified as contradictory.
• Context Precision: The proportion of retrieved context chunks actually used to generate the response, penalizing irrelevant noise. These metrics are aggregated into a single Faithfulness Score for model evaluation.

Beyond factual content, context adherence also applies to structural constraints. When a user provides a specific format or schema in the context, schema-constrained decoding forces the model's token generation to conform to a predefined formal grammar (e.g., a JSON schema or a specific legal clause structure). This prevents the model from hallucinating invalid fields or deviating from the requested output format, ensuring the response is not only factually grounded but also structurally compliant with the input instructions.

A quantitative evaluation framework that measures the factual consistency of a generated summary or answer relative to the source material. In the context of context adherence, a faithfulness metric directly penalizes any generated token that cannot be algorithmically mapped back to the input context.

• Contradiction Detection: Identifies statements that oppose the source
• Extrinsic Hallucination: Flags the introduction of external knowledge
• Intrinsic Hallucination: Detects the misrepresentation of provided facts

The automated process of verifying that every factual claim in a generated text is explicitly supported by the provided source document. This serves as a critical guardrail against hallucination in legal AI by enforcing a strict entailment relationship between the output and the context.

• Claim Decomposition: Breaks output into atomic verifiable facts
• Entailment Scoring: Uses NLI models to check logical support
• Unsupported Span Flagging: Highlights text with no provenance

The capability of an AI system to not only generate an answer but also pinpoint the exact origin of the information. This provides a transparent audit trail from output back to the raw source text, which is a non-negotiable requirement for legal professionals who must validate every assertion.

• Span-Level Linking: Maps output phrases to specific source paragraphs
• Provenance Tracking: Maintains a chain of custody for each fact
• Citation Integrity: Ensures references are not fabricated

A classification task that determines whether a hypothesis (the generated statement) can be logically inferred from a premise (the source context). In legal AI, this is the core computational engine for context adherence, classifying each claim as entailed, contradicting, or neutral.

• Entailment: The hypothesis is a logical consequence of the premise
• Contradiction: The hypothesis negates the premise
• Neutral: The hypothesis introduces information not in the premise

A generation technique that forces a language model to output tokens that conform to a predefined formal grammar or JSON schema. This prevents structural hallucinations and ensures the model does not invent new fields or formats, keeping the output strictly within the boundaries defined by the system architect.

• Context-Free Grammar (CFG) Enforcement: Masks invalid next tokens
• JSON Mode: Guarantees valid structured output
• Lexical Constraint: Prevents the model from drifting outside the defined vocabulary of the task

A set of statistical techniques that enable a model to estimate the confidence of its own predictions. For context adherence, a high uncertainty score on a generated claim signals that the model is likely guessing or relying on internal knowledge rather than the provided context, triggering a human review or abstention.

• Predictive Entropy: Measures the spread of the model's output distribution
• Conformal Prediction: Provides a statistically rigorous guarantee of coverage
• Abstention Trigger: Automatically flags low-confidence outputs for review