Production and Perception in LLMs: A Token Probability Approach

2026-07-13 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

An exploratory study investigated whether large language models (LLMs) exhibit a functional distinction between language production and perception, analogous to psycholinguistics. Researchers operationalized this by measuring direct token probabilities, rather than using metalinguistic prompting. Using the base Llama-3.1-8B model, poems were generated with a production prompt, and the tokens were then re-scored under both rephrased production prompts and perception-oriented prompts. Across an extended experiment with four production and three perception prompts, production-perception distances consistently and substantially exceeded production-production distances, showing an average ratio of approximately 1.8. This effect, specific to communicative framing, replicated across five open-weight models, including Llama-3.1-8B, EuroLLM-9B, gemma-2-9b-it, Mistral-7B-Instruct-v0.3, and Qwen2.5-7B-Instruct. Temporal analysis indicated the perception prompt's influence is strongest early in the sequence, decaying as context accumulates. These findings suggest prompt framing alone induces this distinction in LLM probability distributions, even in decoder-only architectures.

Key takeaway

For NLP Engineers designing LLM applications, understanding prompt framing's impact on token probabilities is crucial. Your choice of production versus perception-oriented prompts significantly alters model behavior, even in decoder-only architectures. Consider explicitly testing different prompt types to optimize for desired output characteristics, especially early in generated sequences where the perception prompt's influence is strongest. This can refine control over LLM responses.

Key insights

Prompt framing alone induces a production-perception distinction in LLM token probability distributions.

Principles

• LLMs exhibit production-perception asymmetry.
• Communicative prompt framing induces this distinction.
• Effect is independent of prompt surface variation.

Method

Poems were generated with production prompts, then tokens re-scored under rephrased production and perception prompts using direct token probability measurements.

In practice

• Vary prompt framing to influence LLM output.
• Consider prompt type for specific generation tasks.
• Analyze token probabilities for behavioral insights.

Topics

• LLM Production-Perception
• Prompt Framing
• Token Probability
• Llama-3.1-8B
• Decoder-only LLMs
• Psycholinguistic Asymmetry

Best for: Research Scientist, AI Engineer, Machine Learning Engineer, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.