Phase 3: Transformers for NLP · ~60 minutes · Python

Natural Language Inference — Textual Entailment

"t entails h" means a human reading t would conclude h is true. NLI is the task of predicting entailment / contradiction / neutral. Boring on the surface, load-bearing in production.

Hiring signal: Understanding of natural language inference — textual entailment internals

What you will learn

Implement natural language inference — textual entailment from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Learn Languages: Python Prerequisites: Phase 5 · 05 (Sentiment Analysis), Phase 5 · 13 (Question Answering) Time: ~60 minutes

The Problem

You built a summarizer. It produced a summary. How do you know the summary does not contain a hallucination?

You built a chatbot. It answered "yes." How do you know the answer is supported by the retrieved passage?

You need to classify 10,000 news articles by topic. You have no training labels. Can you reuse a model?

All three problems reduce to Natural Language Inference. NLI asks: given a premise t and a hypothesis h, is h entailed by t, contradicted, or neutral (unrelated)?

Hallucination check: t = source document, h = summary claim. Not entailment = hallucination.
Grounded QA: t = retrieved passage, h = generated answer. Not entailment = fabrication.
Zero-shot classification: t = document, h = verbalized label ("This is about sports"). Entailment = predicted label.

One task, three production uses. This is why every RAG evaluation framework ships an NLI model under the hood.

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