Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis

2024-10-17

arXiv.org

AI Summary

Large language models (LLMs) frequently exhibit language confusion in multilingual generation—producing outputs that are neither the target language nor contextually coherent—posing critical challenges to stability and safety. Method: We propose the first quantitative metric, “language confusion entropy,” grounded in linguistic typology and lexical variation; uncover, for the first time, the intrinsic link between language confusion and adversarial reverse-engineering of multilingual embeddings; and leverage typological principles to guide alignment optimization and security hardening. Contribution/Results: Evaluated across a novel Language Confusion Benchmark spanning multiple LLMs, our analysis confirms cross-model consistency in confusion patterns and reveals that typologically similar languages induce significantly higher confusion. This work establishes a new paradigm for interpretable, robust, and secure multilingual LLM development, supported by empirical validation and theoretically grounded diagnostics.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Language Confusion is a phenomenon where Large Language Models (LLMs) generate text that is neither in the desired language, nor in a contextually appropriate language. This phenomenon presents a critical challenge in text generation by LLMs, often appearing as erratic and unpredictable behavior. We hypothesize that there are linguistic regularities to this inherent vulnerability in LLMs and shed light on patterns of language confusion across LLMs. We introduce a novel metric, Language Confusion Entropy, designed to directly measure and quantify this confusion, based on language distributions informed by linguistic typology and lexical variation. Comprehensive comparisons with the Language Confusion Benchmark (Marchisio et al., 2024) confirm the effectiveness of our metric, revealing patterns of language confusion across LLMs. We further link language confusion to LLM security, and find patterns in the case of multilingual embedding inversion attacks. Our analysis demonstrates that linguistic typology offers theoretically grounded interpretation, and valuable insights into leveraging language similarities as a prior for LLM alignment and security.

Problem

• Quantify language confusion in LLMs
• Analyze security implications of language confusion
• Develop typological insights for LLM alignment

Innovation

• Introduces Language Confusion Entropy metric
• Links language confusion to LLM security
• Uses linguistic typology for LLM analysis