Llama-3.1 shows production-perception gap via token probability analysis
Researchers directly measured token probabilities in Llama-3.1-8B to test whether LLMs exhibit the production-perception asymmetry documented in human psycholinguistics. By scoring identical tokens under different prompt framings, the study moves beyond metalinguistic self-report to empirical probability measurement. The finding matters for interpretability: if LLMs show systematic divergence between how they generate versus evaluate text, it suggests the unified next-token prediction mechanism masks functionally distinct internal states. This has implications for alignment, probing reliability, and understanding what LLM confidence scores actually measure.
Modelwire context
ExplainerThe study's key move is methodological: rather than asking models to report their own uncertainty, researchers directly scored token probabilities across prompt variations to catch systematic divergence. This sidesteps the self-report problem that plagues confidence measurement.
This connects directly to the distributed backdoor work from earlier this month (cs.LG, July 13), which exposed how safety monitors miss harm when it's fragmented across components. If LLMs mask functionally distinct internal states behind a unified next-token mechanism, as this paper suggests, then probes designed to detect misalignment or deception may be measuring the wrong thing entirely. The production-perception gap means a model could generate one output while internally assigning high probability to alternatives, making behavioral audits unreliable. This also echoes the RAG bias paper from the same day: if confidence scores don't reflect actual model uncertainty, then systems relying on those scores to gate retrieved information may fail silently.
If follow-up work shows the production-perception gap correlates with specific attention head patterns or layer activations in Llama-3.1-8B, that confirms the asymmetry is mechanistically real rather than a measurement artifact. If the effect disappears in larger models (Llama-3.1-70B or beyond), that suggests it's a scaling phenomenon worth tracking for alignment implications.
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Production and Perception in LLMs: A Token Probability Approach”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.