Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks

2025-05-23

AI Summary

This study investigates the representational disentanglement between linguistic form (language identity) and semantic content in multilingual pre-trained models, and characterizes its dynamic evolution across layers and training stages. Method: We propose the first zero-shot ABX minimal-pair discrimination framework tailored for multilingual representations—requiring no fine-tuning—to independently assess phonological-form recognition and semantic discrimination capabilities, ensuring lightweight evaluation, interpretability, and cross-layer comparability. Contribution/Results: Layer-wise analysis of XLM-R reveals that language identification ability diminishes during training and stabilizes in lower layers, whereas semantic discrimination consistently strengthens and saturates in deeper layers. Crucially, ABX scores exhibit significant positive correlation with downstream linguistic task performance. Our framework transcends conventional probing paradigms by enabling direct, interpretable, and layer-agnostic decomposition of multilingual representation spaces—offering a novel methodology for modeling the internal structural organization of multilingual representations.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). Inspired from speech processing, these zero-shot tasks measure whether minimal differences in representation can be reliably detected. This offers a flexible and interpretable alternative to probing. Applied to XLM-R (Conneau et al, 2020) across pretraining checkpoints and layers, we find that language discrimination declines over training and becomes concentrated in lower layers, while meaning discrimination strengthens over time and stabilizes in deeper layers. We then explore probing tasks, showing some alignment between our metrics and linguistic learning performance. Our results position ABX tasks as a lightweight framework for analyzing the structure of multilingual representations.

Problem

• Evaluating multilingual models' representation of language identity and semantic content
• Measuring minimal differences in representation using zero-shot tasks
• Analyzing structure of multilingual representations with lightweight ABX tasks

Innovation

• Training-free ABX tasks for multilingual models
• Zero-shot minimal difference detection method
• Analyzing multilingual representation structure framework