Contradiction Detection

The foundational architecture for contradiction detection. An NLI model classifies the relationship between a premise (source document) and a hypothesis (generated statement) as entailment, neutral, or contradiction.

• Fine-tuned models: BART, T5, or DeBERTa fine-tuned on MNLI or ANLI datasets.
• Mechanism: Computes a softmax probability over the three classes.
• Thresholding: A statement is flagged if the contradiction probability exceeds a set threshold (e.g., >0.9).

Identifies mutually exclusive facts spread across multiple source documents. This is critical for multi-document synthesis where different reports may state conflicting figures.

• Entity-centric grouping: Clusters all statements about a specific entity (e.g., 'Q3 Revenue').
• Numerical contradiction: Flags if Document A states '$2M' and Document B states '$2.5M' for the same metric.
• Temporal scoping: Resolves conflicts by checking if statements refer to the same time period.

Identifies logical inconsistencies within a single generated response, a common failure mode in long-form generation.

• Intra-sentence analysis: Scans for opposing polarity (e.g., 'The system is active' vs. 'The system is dormant').
• Long-range dependency: Uses a sliding window or graph-based attention to detect contradictions separated by paragraphs.
• Mitigation: Triggers a regeneration loop with a specific prompt to resolve the internal conflict before showing the user.

An automated metric that quantifies the alignment between a generated summary and its source. Unlike simple NLI, this produces a fine-grained score.

• Atomic Fact Decomposition: Breaks the summary into minimal, verifiable claims.
• Per-Claim Verification: Runs NLI on each atom against the source.
• Aggregation: Calculates the ratio of supported claims to total claims. A score of 1.0 indicates perfect consistency.

Specialized logic to identify conflicts in chronological sequences. Standard NLI often fails here because a statement can be true at one time and false at another.

• Time expression normalization: Maps 'last Tuesday' and 'March 5th' to a standard ISO timestamp.
• Temporal relation extraction: Classifies event ordering (before, after, overlap).
• Rule-based checks: Flags violations like 'CEO resigned in June' vs. 'CEO approved the deal in July'.

A prompting framework that enables a model to self-correct contradictions without external tools. The model acts as its own critic.

• Draft Phase: Generate the initial response.
• Verify Phase: Prompt the model to generate a list of verification questions based on the draft.
• Execute Phase: Answer those questions independently using the source text.
• Correct Phase: Compare answers to the draft and rewrite any inconsistent statements.