Short definition:
Natural Language Inference (NLI) is a type of AI task where the model determines whether a given statement logically follows, contradicts, or is unrelated to another statement.
In Plain Terms
NLI is about teaching AI to reason with language, not just repeat it.
The system is given two sentences:
- A premise (e.g. “The company increased its revenue last year.”)
- A hypothesis (e.g. “The company’s performance got worse.”)
The AI must decide if the second sentence is:
- Entailed (it logically follows)
- Contradicted (it conflicts with the first)
- Neutral (it’s unrelated or unclear)
This kind of understanding helps AI go beyond keywords and truly grasp meaning and logic.
Real-World Analogy
It’s like asking a junior employee:
“You read that report — based on it, do you think sales are up or down?”
If they can explain why one sentence supports, contradicts, or says nothing about another, they’re doing NLI. AI is learning to do the same.
Why It Matters for Business
- Improves the quality of AI answers
NLI helps chatbots and assistants answer with better reasoning — not just repeating phrases. - Enables smart document summarization and comparison
AI can identify when a new report supports or contradicts previous findings. - Useful for fact-checking, legal, and finance tools
Systems can spot inconsistencies between contracts, terms, or financial statements.
Real Use Case
A legal tech firm uses NLI to scan clauses in NDAs and automatically flag when a new contract conflicts with standard terms.
The AI doesn’t just match keywords — it understands what the clause actually means.
Related Concepts
- Natural Language Understanding (NLU) (NLI is one advanced form of NLU)
- Semantic Search (Uses NLI to find documents that are logically related, not just similar in wording)
- Fact Checking AI (Relies on NLI to validate claims)
- Text Summarization (Can be enhanced with inference-based logic)
- AI Reasoning(NLI is a building block of more complex reasoning capabilities in AI models)
