S 2 LPP : Small-to-Large Prompt Prediction across LLM s (2025)

Abstract: The performance of pre-trained Large Language Models (LLMs) is often sensitive to nuances in prompt templates, requiring careful prompt engineering, adding costs in terms of computing and human effort. In this study, we present experiments encompassing multiple LLMs variants of varying sizes aimed at probing their preference with different prompts. Through experiments on Question Answering, we show prompt preference consistency across LLMs of different sizes. We also show that this consistency extends to other tasks, such as Natural Language Inference. Utilizing this consistency, we propose a method to use a smaller model to select effective prompt templates for a larger model. We show that our method substantially reduces the cost of prompt engineering while consistently matching performance with optimal prompts among candidates. More importantly, our experiment shows the efficacy of our strategy across fourteen LLMs and its applicability to a broad range of NLP tasks, highlighting its robustness.

## introduction recent research @xcite @xcite @xcite @xcite has demonstrated that prompting is crucial to the downstream performance of foundation llms, requiring efficiently prompt engineering for practical applications. while manually crafted prompts @xcite have been widely used, @xcite introduced an automated method for creating prompts for various tasks using a gradientguided search. however, the method requires iterative refinement for the prompts, which would be prohibitively expensive for current llms. also, their assumption of access to llm logit outputs is invalid for black-box llms. with the advancement of llms, @xcite , @xcite , and @xcite have leveraged llms to generate instruction candidates and have selected prompts by optimizing a chosen score function. these methods require calculating the score across all candidate prompts using large-sized llms to reach optimal performance for each task, which is also computationally expensive. what is worse, the rapid evolution of llms also might appear to pose challenges in efficiently updating the prompt template selections for new emerging llms. to ascertain whether llms of different sizes exhibit similar preferences for various prompts, we introduce a series of experiments by generating multiple natural language prompts for question answering (qa) and then extends to natural language inference (nli) tasks. we evaluate these prompts across a range of llms of varying sizes. our studies prove that various llms consistently select identical optimal prompts from the pool of candidate prompts. based on our findings, we exploit the prompt preferences of smaller models as proxies to that of larger models. with smaller models, it is less computationally expensive to gain knowledge of their prompt preference. we propose a small-to-large prompt prediction (s2lpp) approach, leveraging smaller models to identify optimal prompt templates from automatically generated prompt candidates for larger target models. this approach would help to reduce the deployment cost of llms, especially when faced with diverse and dynamic sets of open-domain knowledge. we show the effectiveness of the s2lpp approach on open-domain qa and nli across fourteen llms of varying sizes, and further extend it to broader nlp tasks such as retrieval-augmented generation and arithmetic reasoning, showcasing its robustness and generalizability. the main contributions of this paper can be summarized as follows: ## background the performance of contemporary llms heavily depends on the forms and nuances present in the natural language prompts they are given @xcite @xcite @xcite . however, owing to the black-box nature of llms, their prompt preference is also underexplained and sometimes dependent on nuanced variations @xcite @xcite , requiring extensive prompt engineering to achieve optimal performance for each task. ## consistency of prompt preferences across different model sizes in this section, we analyze consistency in prompt preference among llms of varying sizes. we set up a series of experiments on two tasks: opendomain qa ( §3.1) and nli ( §3.2), respectively, which pose challenges to the current state-of-theart llms. first, we collect multiple natural language prompt templates for qa and nli. then, we evaluate these prompts across llms of varying sizes, comparing their performance to determine whether models from the same family, despite differences in scale, exhibit similar preferences for the best-performing prompt. models: in our experiments, we evaluate multiple prompt templates on deepseek-r1 (deepseek-ai et al., 2025), llama-2-chat (touvron et al., 2023), llama-3-instruct (ai@meta, 2024), and vicuna (zheng et al., 2023) model families, using models of varying sizes within each family. datasets task samples prompt source num of relations num of prompts prompt description google-re qa 5.5k auto-generated 3 10 per relation a natural question to describe a relation, like placeofbirth. e.g. "what is the birthplace of [x]?" t-rex qa 31k auto-generated 41 10 per relation levy/holt nli 1.8k manual-generated 1 5 a binary question to judge if [premise] entails [hypothesis]. e.g. "if google bought youtube, then google owns youtube" each premise and hypothesis is also structured as a relation triple, containing a single predicate with two entity arguments, wherein identical entities are present in both the premise and the hypothesis. a distinctive feature of the levy/holt dataset is the inclusion of inverse pairs for all premise-hypothesislabel entailments. following prior work @xcite @xcite , we study the challenging directional subset, where the entailments hold in one direction but not both. prompt candidates: we employ the same prompts utilized in prior work @xcite for evaluation, consisting of five natural question prompts crafted by human experts. we present the manually crafted prompts in appendix b and the detailed experimental settings in ## small-to-large prompt prediction the previous experiments in §3 have shown the existence of consistency in prompt preference among various sizes of llms. in this section, we exploit this consistency to reduce the development cost of llms in nlp applications. we propose the small-to-large prompt prediction (s2lpp) method, leveraging this consistency to automatically generate and select high-performing prompts for new, unseen opendomain knowledge in a computationally efficient manner. we evaluate s2lpp on open-domain qa and nli tasks and extend the pipeline to a wider range of nlp applications, including using smaller llms for retrieved document selection in open-domain qa and for chain-of-thought (cot) prompt selection in arithmetic reasoning tasks. ## general discussion the common factor across the set of models is the similarity in the distributions of their pretraining corpora, so we conjecture that this promptpreference consistency originates from the pretraining and that the prompt templates best aligned with the pre-training distribution would prevail. this also explains the differences between the finding in @xcite and us, where they used rigid templates, and we used organic, natural language prompts, which more closely resemble the pre-training conditions of various llms. the s2lpp approach demonstrates the efficacy of exploiting the consistency of prompt preference and offers an efficient method for prompt selection using small-sized models, which can complement sota prompt generation methods. additionally, the prompt-selection models can be seamlessly updated with newly released llms. with the assumption that this prompt preference consistency originates from pre-training, the prompts selected by previous prompt-selection models could be performant with newly released target llms as well. ## conclusion across several major llm families and experimental settings, we have demonstrated the consistency of prompt preference across llms on the qa and nli tasks, providing significant potential for applications. our work represent a finding that llms from the same model family, regardless of size, exhibit similar preferences across different prompts. based on this finding, we further propose a lightweight approach to utilize the consistency of prompt preference for open-domain questions involving new, unseen knowledge, by exploiting smaller models to select highly performant prompts at minimal cost in computation. we validate the efficacy of the approach in qa and nli. experiments demonstrate that the prompt templates selected with our strategy outperform baselines. our methods also possess a strong capability to recover the performance of oracle prompts with significantly lower costs in the prompt selection steps. we further present the generalizability of our method to a broader range of nlp tasks. deeper investigations into the source of this consistency will be important directions for our future work.