Entailed Between the Lines: Incorporating Implication into NLI

TL;DR

This paper emphasizes the importance of understanding implied meaning in natural language inference, introduces a new dataset to improve models' recognition of implied entailment, and demonstrates how fine-tuning on this dataset enhances model performance.

Contribution

It formalizes implied entailment as an extension of NLI and creates the INLI dataset to improve models' ability to recognize implicit meanings.

Findings

LLMs fine-tuned on INLI better recognize implied entailment

Models generalize understanding of implied entailment across datasets

INLI dataset expands the scope of NLI tasks

Abstract

Much of human communication depends on implication, conveying meaning beyond literal words to express a wider range of thoughts, intentions, and feelings. For models to better understand and facilitate human communication, they must be responsive to the text's implicit meaning. We focus on Natural Language Inference (NLI), a core tool for many language tasks, and find that state-of-the-art NLI models and datasets struggle to recognize a range of cases where entailment is implied, rather than explicit from the text. We formalize implied entailment as an extension of the NLI task and introduce the Implied NLI dataset (INLI) to help today's LLMs both recognize a broader variety of implied entailments and to distinguish between implicit and explicit entailment. We show how LLMs fine-tuned on INLI understand implied entailment and can generalize this understanding across datasets and domains.