Natural Language Inference (NLI), or Recognizing Textual Entailment (RTE), is a fundamental NLP task where the model determines the logical relationship between a "premise" sentence and a "hypothesis" sentence — typically classifying the relationship as Entailment (true), Contradiction (false), or Neutral (unrelated).

The Logic Classes

• Entailment: If Premise is true, Hypothesis MUST be true. ("He was murdered" entails "He is dead").
• Contradiction: If Premise is true, Hypothesis MUST be false. ("It is raining" contradicts "It is sunny").
• Neutral: The truth of Hypothesis cannot be determined from Premise. ("He loves cats" is neutral to "He loves dogs").

Why It Matters

• Deep Understanding: Requires reasoning, not just keyword matching.
• Zero-Shot Classification: NLI models can be used for zero-shot classification by framing labels as hypotheses ("This text is about sports.").
• Benchmarks: MNLI, SNLI, ANLI are key benchmarks for model reasoning capability.

Natural Language Inference is the logic test — determining whether one statement follows logically from another, the bedrock of textual reasoning.