Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing
arXiv:2510.04120v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing. We present a diagnostic analysis that examines the limits of behavioral evidence by probing three complementary dimensions: semantic attribute alignment, lexical invariance, and syntactic sensitivity. Using geometric probing, we assess whether model-generated interpretations align with reference semantic attributes; through context-varying substitut
The proliferation and increasing sophistication of large language models necessitate deeper understanding of their cognitive mechanisms and limitations.
Understanding how LLMs process complex language like metaphor is crucial for developing more robust, reliable, and human-aligned AI agents.
This research provides a diagnostic framework to move beyond purely behavioral assessment, offering tools to analyze internal LLM mechanics related to semantic alignment, lexical invariance, and syntactic sensitivity.
- · AI researchers
- · NLP developers
- · AI ethics and safety organizations
Improved diagnostic tools lead to a more nuanced understanding of LLM capabilities beyond simple performance metrics.
This understanding informs the design of next-generation LLM architectures that more effectively handle complex linguistic phenomena and reduce unforeseen biases.
More explainable and predictable LLMs accelerate the deployment of autonomous AI agents in sensitive domains, provided these insights are effectively operationalized.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI