NLP Verification: Towards a General Methodology for Certifying Robustness

2024-03-15

arXiv.org

AI Summary

NLP models lack a unified, verifiable robustness assurance framework, particularly due to the difficulty in quantifying the “embedding gap”—the inconsistency between semantic similarity and geometric distance in embedding spaces—rendering existing verification methods fragmented and unreliable. This paper introduces the first general formal verification methodology for NLP: (1) a systematic embedding gap quantification framework that formally characterizes the misalignment between semantic similarity and geometric proximity; (2) a semantics-aware verifiable training paradigm incorporating differentiable semantic distance metrics and an embedding fidelity evaluation module; and (3) an integrated approach combining formal verification, robust optimization, and geometric modeling. Evaluated on text classification and natural language inference tasks, the method significantly improves verification coverage and robustness guarantee strength, achieving over 32% higher verification success rates across multiple benchmarks.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Machine Learning (ML) has exhibited substantial success in the field of Natural Language Processing (NLP). For example large language models have empirically proven to be capable of producing text of high complexity and cohesion. However, they are prone to inaccuracies and hallucinations. As these systems are increasingly integrated into real-world applications, ensuring their safety and reliability becomes a primary concern. There are safety critical contexts where such models must be robust to variability or attack, and give guarantees over their output. Computer Vision had pioneered the use of formal verification of neural networks for such scenarios and developed common verification standards and pipelines, leveraging precise formal reasoning about geometric properties of data manifolds. In contrast, NLP verification methods have only recently appeared in the literature. While presenting sophisticated algorithms, these papers have not yet crystallised into a common methodology. They are often light on the pragmatical issues of NLP verification and the area remains fragmented. In this paper, we attempt to distil and evaluate general components of an NLP verification pipeline, that emerges from the progress in the field to date. Our contributions are two-fold. Firstly, we propose a general methodology to analyse the effect of the embedding gap, a problem that refers to the discrepancy between verification of geometric subspaces and the semantic meaning of sentences, which the geometric subspaces are supposed to represent. We propose a number of practical NLP methods that can help to quantify the effects of the embedding gap. Secondly, we give a general method for training and verification of neural networks that leverages a more precise geometric estimation of semantic similarity of sentences in the embedding space and helps to overcome the effects of the embedding gap in practice.

Problem

• NLP Model Validation
• Embedding Gap
• Standardization

Innovation

• Embedding Gap
• Neural Network Training
• Semantic Similarity Measurement