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Prompt Repetition Improves Non-Reasoning LLMs (arxiv.org)
2 points by ksec 280 days ago | hide | past | pdf | 1 comment on HN

In plain words: Simply writing the same prompt more than once before asking for an answer makes popular chat models answer better when they are not thinking step by step. It beats the usual single prompt without generating extra words or taking longer.

Abstract

When not using reasoning, repeating the input prompt improves performance for popular models (Gemini, GPT, Claude, and Deepseek) without increasing the number of generated tokens or latency.

Yaniv Leviathan, Matan Kalman, Yossi Matias
arXiv:2512.14982 · cs.LG, cs.AI, cs.CL · submitted Dec 17, 2025
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This has direct implications for my axiomatic prompting research (https://osf.io/pcx2d). We found that providing LLMs with explicit classification axioms improved LEDGAR accuracy by 10 to 22 percentage points across 7/8 models. But axioms add substantial tokens to the prompt.

The question I can't currently answer: how much of the benefit comes from the semantic content of the axioms versus the repetition/emphasis effect this paper identifies?

I'm running an ablation study with a critical condition: shuffled axioms (same tokens, randomized order). If shuffled matches structured axioms, the content doesn't matter. If structured axioms win, semantic structure genuinely helps beyond repetition.

I'll add this experiment to the parent project on OSF. Curious whether others working on knowledge injection techniques have similar confounds to untangle.