natural language inference

**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.