Entailed Between the Lines: Incorporating Implication into NLI

2025-01-13

AI Summary

Existing natural language inference (NLI) models struggle to identify implicit entailment—i.e., pragmatic, context-dependent inferences not directly expressed in the text—limiting their capacity for deep semantic and pragmatic reasoning. This work formally defines the implicit entailment recognition task and introduces INLI, the first benchmark dataset explicitly annotated to distinguish implicit from explicit entailments. We propose a multi-source collaborative annotation and evaluation framework, integrating large language model fine-tuning, fine-grained entailment relation modeling, and cross-domain generalization design. Experiments demonstrate that our model achieves significant gains in implicit entailment accuracy on INLI, while also exhibiting strong generalization across diverse external NLI benchmarks and domain-specific texts. This work establishes a new benchmark and methodological foundation for advancing NLI beyond surface-level logical inference toward deeper pragmatic reasoning.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Natural Language Inference
• Implication Understanding
• Indirect Meaning Recognition

Innovation

• INLI dataset
• Natural Language Inference
• Implied Expression Understanding