prompt-design-at-scale-how-format-instruction-count-and-context-length-shape-instruction-adherence-and-hallucination-in-large-language-models-dda08c0e·1 events·first seen Aliases: Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models
A new arXiv preprint reports two controlled experiments examining how prompt format (markdown, plain text, prose, tabular), instruction count, and context length affect instruction-following and hallucination across five LLMs. Key findings: perfect instruction-following collapses to zero by N=80 rules regardless of format or model; recall holds through 64-128k tokens then degrades sharply and format-dependently; fabrication is near-zero but refusal rates spike to 79-90% near context ceilings. The authors release VeyraBench, a contamination-free synthetic corpus of 8,780 uniquely-named entities, along with the full experimental harness.