Instructions Shape Production of Language, not Processing
- URL: http://arxiv.org/abs/2605.11206v2
- Date: Wed, 13 May 2026 08:57:14 GMT
- Title: Instructions Shape Production of Language, not Processing
- Abstract summary: Instructions trigger a production-centered mechanism in language models.<n>We reveal this mechanism as an asymmetry between the two stages by probing task-specific information layer-wise.<n>Our findings suggest that understanding model capabilities requires jointly assessing internals and behavior.
- Score: 26.122385256608208
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- When Tokenization is Secretly Output Supervision [10.546694596577057]
Tokenization in language models is treated by default as an input preprocessing decision.<n>In autoregressive models, tokenizer granularity determines what the model must resolve in a single forward pass.<n>This affects both the difficulty of the learning problem and the representations that emerge inside the model.
arXiv Detail & Related papers (2026-09-01T15:16:19Z) - Decomposing Reasoning Efficiency in Large Language Models [2.4149105714758545]
We decompose token efficiency into interpretable factors: completion under a fixed token budget, conditional correctness given completion, and verbosity.<n>When reasoning traces are available, we add deterministic trace-quality measures to separate looping from verbose-but-engaged reasoning.<n>Our decomposition reveals distinct bottleneck profiles that suggest different efficiency interventions.
arXiv Detail & Related papers (2026-02-10T14:09:18Z) - How Different Tokenization Algorithms Impact LLMs and Transformer Models for Binary Code Analysis [0.0]
Despite its significance, tokenization in the context of assembly code remains an underexplored area.<n>We explore preprocessing customization options and pre-tokenization rules tailored to the unique characteristics of assembly code.<n>We compare tokenizers based on tokenization efficiency, vocabulary compression, and representational fidelity for assembly code.
arXiv Detail & Related papers (2025-11-05T19:45:26Z) - LayerCake: Token-Aware Contrastive Decoding within Large Language Model Layers [53.43862310647276]
Large language models (LLMs) excel at natural language understanding and generation but remain vulnerable to factual errors.<n>We introduce a token-aware, layer-localized contrastive decoding method that aligns specific token types with their most influential transformer layers to improve factual generation.<n>Our method requires no additional training or model modification, and experiments demonstrate that our method consistently improves factuality across multiple LLMs and various benchmarks.
arXiv Detail & Related papers (2025-07-06T14:35:43Z) - Broken Tokens? Your Language Model can Secretly Handle Non-Canonical Tokenizations [83.93566096400723]
We find that instruction-tuned models retain up to 93.4% of their original performance when given a randomly sampled tokenization.<n>Character-level segmentation improves string manipulation and code understanding tasks by up to +14%.<n>Right-aligned digit grouping enhances large-number arithmetic by +33%.
arXiv Detail & Related papers (2025-06-23T18:02:26Z) - What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patterns [28.945432533898444]
Spotlight is a new approach that combines both automation and human analysis.<n>Based on data mining techniques, we automatically distinguish between random (decoding) variations and systematic differences in language model outputs.<n>We show that our token pattern approach helps users understand the systematic differences of language model outputs.
arXiv Detail & Related papers (2025-04-22T11:53:33Z) - Contextualize-then-Aggregate: Circuits for In-Context Learning in Gemma-2 2B [51.74607395697567]
In-Context Learning (ICL) is an intriguing ability of large language models (LLMs)<n>We use causal interventions to identify information flow in Gemma-2 2B for five naturalistic ICL tasks.<n>We find that the model infers task information using a two-step strategy we call contextualize-then-aggregate.
arXiv Detail & Related papers (2025-03-31T18:33:55Z) - Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning [46.43130011147807]
We argue that tokens serving different roles - specifically, reasoning tokens versus boilerplate tokens - differ significantly in importance and learning complexity.<n>We propose a novel Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination.<n>Using SHAD, we propose the Reasoning-highlighted Fine-Tuning (RFT) method, which adaptively emphasizes reasoning tokens during fine-tuning.
arXiv Detail & Related papers (2024-12-19T12:06:24Z) - When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality [19.103130032967663]
Causal models are forced to output one interpretation and continue, whereas models that can revise may edit their previous output as the ambiguity is resolved.
In this work, we look at how restart-incremental Transformers build and update internal states, in an effort to shed light on what processes cause revisions not viable in autoregressive models.
arXiv Detail & Related papers (2024-02-20T16:09:49Z) - Identifying and Analyzing Performance-Critical Tokens in Large Language Models [52.404072802235234]
We study how large language models learn to perform tasks from demonstrations.<n>Our work sheds light on how large language models learn to perform tasks from demonstrations and deepens our understanding of the roles different types of tokens play in large language models.
arXiv Detail & Related papers (2024-01-20T20:55:21Z) - Token-Level Adversarial Prompt Detection Based on Perplexity Measures
and Contextual Information [67.78183175605761]
Large Language Models are susceptible to adversarial prompt attacks.
This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs.
We introduce a novel approach to detecting adversarial prompts at a token level.
arXiv Detail & Related papers (2023-11-20T03:17:21Z) - Explaining Interactions Between Text Spans [50.70253702800355]
Reasoning over spans of tokens from different parts of the input is essential for natural language understanding.
We introduce SpanEx, a dataset of human span interaction explanations for two NLU tasks: NLI and FC.
We then investigate the decision-making processes of multiple fine-tuned large language models in terms of the employed connections between spans.
arXiv Detail & Related papers (2023-10-20T13:52:37Z) - Self-Attention Attribution: Interpreting Information Interactions Inside
Transformer [89.21584915290319]
We propose a self-attention attribution method to interpret the information interactions inside Transformer.
We show that the attribution results can be used as adversarial patterns to implement non-targeted attacks towards BERT.
arXiv Detail & Related papers (2020-04-23T14:58:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.