Automating Fact-Checking with Multi-Stage Contradiction Detection
Identifying contradictory claims in text goes beyond keyword matching. Statements can be factually inconsistent while using different phrasing, synonyms, or indirect language. Manual verification cannot keep pace with modern content volume. Red Buffer built an NLP pipeline that extracts assertions, generates structured negations, retrieves external evidence, and verifies contradictions using LLM reasoning.
ROLE
Multi-stage NLP pipeline design, assertion extraction, negation generation using rule-based, wildcard, and contextual methods, external evidence retrieval integration, and LLM-based contradiction verification.
TOOL
Python, spaCy, Hugging Face Transformers, PyTorch, GPT-3.5 API
DURATION
Single-phase research and implementation with iterative model tuning and evaluation.
Our Approach
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Assertion Extraction
Used spaCy and transformer-based models to identify and extract clear verifiable assertions from unstructured text isolating the specific claims that require fact checking.
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Multi-Type Negation Generation
Generated rule-based, wildcard, and contextual negations for each assertion enabling the system to detect both direct contradictions and nuanced inconsistencies.
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External Evidence Retrieval
Queried external web sources and knowledge bases to collect supporting or opposing evidence grounding verification in real world information rather than model assumptions.
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LLM-Based Contradiction Verification
Applied GPT-3.5 reasoning to analyze retrieved evidence and determine whether a true contradiction exists accounting for semantic meaning and contextual interpretation.
Why It Matters
This multi-stage approach combining assertion extraction, negation generation, evidence retrieval, and LLM reasoning applies to regulatory compliance monitoring, academic integrity checking, legal discovery, and other domains where identifying inconsistencies across large document collections is critical.
Outcome
Automated Fact-Checking at Scale
Reduced manual verification effort for large volumes of content.
Improved Detection of Nuanced Contradictions
Contextual negation strategies identified indirect and implicit inconsistencies missed by simple rule-based systems.
High Throughput Processing
Pipeline processed large volumes of articles, reports, and claims efficiently without manual bottlenecks.
Explainable and Traceable Outputs
Structured negations and linked evidence provided clear audit trails for human reviewers.
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