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Instructions shape Production of Language, not Processing

Summary

A new study reveals that instructions in language models primarily affect the output production stage, not the input processing stage. By probing task-specific information layer-wise across five binary judgment tasks, researchers found an asymmetry: information in sample tokens remains stable and weakly correlated with behavior, while information in output tokens varies substantially and strongly correlates with behavior. Attention-based interventions confirm that blocking instruction flow to all subsequent tokens reduces both behavior and information in output tokens, whereas blocking it only to sample tokens has minimal effect. The asymmetry generalizes across model families and tasks, and becomes sharper with model scale and instruction-tuning.

SourcearXiv Computational LinguisticsAuthor: Andreas Waldis, Leshem Choshen, Yufang Hou, Yotam Perlit
Instructions shape Production of Language, not Processing
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[2605.11206] Instructions shape Production of Language, not Processing

[Submitted on 11 May 2026]

Title:Instructions shape Production of Language, not Processing

View a PDF of the paper titled Instructions shape Production of Language, not Processing, by Andreas Waldis and 3 other authors

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Abstract:Instructions trigger a production-centered mechanism in language models. Through a cognitively inspired lens that separates language processing and production, we reveal this mechanism as an asymmetry between the two stages by probing task-specific information layer-wise across five binary judgment tasks. Specifically, we measure how instruction tokens shape information both when sample tokens, the input under evaluation, are processed and when output tokens are produced. Across prompting variations, task-specific information in sample tokens remains largely stable and correlates only weakly with behavior, whereas the same information in output tokens varies substantially and correlates strongly with behavior. Attention-based interventions confirm this pattern causally: blocking instruction flow to all subsequent tokens reduces both behavior and information in output tokens, whereas blocking it only to sample tokens has minimal effect on either. The asymmetry generalizes across model families and tasks, and becomes sharper with model scale and instruction-tuning, both of which disproportionately affect the production stage. Our findings suggest that understanding model capabilities requires jointly assessing internals and behavior, while decomposing the internal perspective by token position to distinguish the processing of input tokens from the production of output tokens.

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2605.11206 [cs.CL]

(or arXiv:2605.11206v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.11206

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Andreas Waldis [view email] [v1] Mon, 11 May 2026 20:21:04 UTC (25,221 KB)

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Key points

  • Instructions trigger a production-centered mechanism in LLMs
  • Task-specific information in sample tokens is stable, but varies in output tokens and correlates with behavior
  • Attention interventions causally show that instructions primarily affect output tokens
  • The production-centered asymmetry increases with model scale and instruction-tuning

Highlights and analysis are generated automatically and may contain errors. Check the original source.