Prompt-format mirroring and structural priming in output
Parent: Instruction-file hygiene for LLM lexical tics: linting, mirroring, hook filtering · Published reference · snapshot 2026-09-23
↓ Facts as markdownall context files
Source-anchored research for Prompt-format mirroring and structural priming in output. Run status: COMPLETED.
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How it works
- Language models show structural priming: they reuse recently seen syntactic structures, including when prime and target share no lexical overlap, indicating abstract structural representations. (high confidence) [source]
Comparisons and alternatives
- Practical mitigation for instruction files: write the instruction file in the plain style wanted in the output (prose, little markdown) and state the desired format positively, rather than only listing prohibitions. (low confidence) [source]
Facts and statements
- Anthropic's prompting guide says the formatting style of a prompt may influence Claude's response style, and that removing markdown from the prompt can reduce the volume of markdown in the output. (medium confidence) (verified as of 2026-09-23) [source]
- Prompt format alone can change measured performance a lot: up to 76 accuracy points on LLaMA-2-13B in few-shot settings, persisting with larger models, more shots, or instruction tuning; format performance correlates only weakly across models. (high confidence) [source]
- Format sensitivity falls with model strength: GPT-4 variants varied less across plain text, markdown, JSON and YAML than GPT-3.5-turbo, and no single format is best across models. (medium confidence) [source]
- A 2026 study (9,649 experiments, 11 models, YAML/markdown/JSON/TOON context) found no significant aggregate accuracy effect of context format (p=0.484), though individual models, mostly open-source, showed format-specific differences. (low confidence) (verified as of 2026-02-01) [source]
Children
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