Reverse-engineering NLI: A study of the meta-inferential properties of Natural Language Inference

2026-01-08

arXiv.org

AI Summary

This study addresses the long-standing ambiguity in the logical interpretation of labels in natural language inference (NLI) tasks, which has led to biased assessments of model performance. The work proposes and empirically validates three distinct logical interpretations of the standard NLI label set. By leveraging examples with shared premises and data generated by large language models, the authors construct a meta-reasoning consistency evaluation framework. Applying this framework to the SNLI dataset reveals a strong alignment with one specific logical interpretation, offering crucial empirical evidence for understanding the intrinsic nature of NLI tasks and the reasoning behaviors of current models. This finding provides a principled basis for re-evaluating model capabilities and dataset design in NLI research.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Natural Language Inference (NLI) has been an important task for evaluating language models for Natural Language Understanding, but the logical properties of the task are poorly understood and often mischaracterized. Understanding the notion of inference captured by NLI is key to interpreting model performance on the task. In this paper we formulate three possible readings of the NLI label set and perform a comprehensive analysis of the meta-inferential properties they entail. Focusing on the SNLI dataset, we exploit (1) NLI items with shared premises and (2) items generated by LLMs to evaluate models trained on SNLI for meta-inferential consistency and derive insights into which reading of the logical relations is encoded by the dataset.

Problem

• Natural Language Inference
• meta-inferential properties
• logical relations
• SNLI dataset
• inference interpretation

Innovation

• Natural Language Inference
• meta-inferential consistency
• logical relations
• SNLI dataset
• reverse-engineering