Post-Hoc Understanding of Metaphor Processing in Decoder-Only Language Models via Conditional Scale Entropy
Researchers have developed conditional scale entropy, a wavelet-based diagnostic tool that reveals how decoder-only transformers process metaphorical language across layers. The method isolates structural computation patterns from magnitude, showing that metaphorical tokens consistently activate broader frequency spectra than literal ones across model scales from 124M to 20B parameters. This mechanistic finding advances interpretability by pinpointing where and how models resolve semantic divergence, offering a replicable probe for understanding non-literal reasoning in production architectures.
Modelwire context
ExplainerThe key detail the summary underplays is the wavelet framing itself: by decomposing activations into frequency components across layers rather than inspecting attention weights or probing classifiers, the method sidesteps a persistent criticism of interpretability work, which is that most probes measure correlation with surface features rather than computation structure. The scale-invariance claim across 124M to 20B parameters is the part that needs scrutiny.
This connects most directly to the coverage of 'Quantifying the cross-linguistic effects of syncretism on agreement attraction,' which also used attention entropy as a diagnostic lens on model internals to surface psycholinguistic phenomena. Both papers treat LLM internals as measurement instruments rather than black boxes, and both make claims about what those measurements reveal about language cognition. The difference is that the syncretism paper validated its metrics against behavioral data across languages, while the metaphor paper has no comparable external ground truth for what 'correct' metaphor processing looks like, which is a real gap.
If independent groups replicate the broader-frequency-spectrum finding on models outside the GPT-2 and LLaMA-2 families, particularly on instruction-tuned variants, the structural claim becomes credible. If the pattern collapses under fine-tuning, it suggests the signal is an artifact of pretraining data distribution rather than a general property of metaphor resolution.
Coverage we drew on
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MentionsGPT-2 · LLaMA-2 · conditional scale entropy · decoder-only language models
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