arXiv:2601.03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large language models (LLMs)' reasoning pathways, given that their training data contain a large number of metaphors. In this work, we investigate the problem in the scope of the emergent misalignment problem, where LLMs can generalize patterns learned from misaligned content in one domain to another domain. We find strong evidence that metaphors in training data contribute to cross-domain misalignment in L
This research provides a deeper, technical understanding of emergent misalignment in AI, which is a growing concern as large language models become more ubiquitous and influential.
Understanding how metaphors in training data contribute to cross-domain misalignment is critical for developing more robust, reliable, and ethically sound AI systems, particularly as AI integrates into sensitive decision-making processes.
The focus for addressing AI misalignment may shift to include more nuanced analysis of training data composition, specifically how figurative language can propagate undesirable reasoning patterns across domains.
- · AI safety researchers
- · Developers of AI alignment techniques
- · Frameworks for explainable AI
- · Developers neglecting data-centric AI alignment
- · Unregulated deployment of LLMs in critical domains
AI developers will need to implement more sophisticated data auditing and filtering methods to reduce metaphorical influence on reasoning.
This could lead to the development of new parsing and interpretation layers in LLMs designed to explicitly identify and manage metaphorical language.
The broader societal trust in AI systems could either increase due to enhanced robustness or decrease if these misalignment issues are found to be more pervasive and difficult to mitigate.
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Read at arXiv cs.LG