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    <abstract>Prompt optimization (PO) generates prompts to guide Large Language Models (LLMs) in performing tasks. Existing methods, such as PromptAgent, rely on a single static prompt, which struggles with disjoint cases in complex tasks. Although MoP uses multiple prompts, it fails to account for variations in task complexity. Inspired by programmatic control flow, we introduce a nested if-else structure to address both varying similarities and complexities across diverse cases. We propose Tree-of-Prompts (ToP), which implements this structure by recursively expanding child prompts from a parent prompt. Sibling prompts tackle disjoint cases while inheriting shared similarities from their parent, and handle cases more complex than the parent. Evaluated on Gorilla (understanding), MATH (reasoning), and a subset of BBH benchmarks, ToP outperforms PromptAgent and MoP, with improvements of 1.4% and 4.6% over PromptAgent and 3.2% and 4.5% over MoP, when tested with GPT-4o-mini and Llama 3.2-3B, respectively.</abstract>
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%0 Conference Proceedings
%T Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization
%A Kim, Jihyuk
%A Garg, Shubham
%A Poddar, Lahari
%A Hwang, Seung-won
%A Hench, Chris
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F kim-etal-2025-tree
%X Prompt optimization (PO) generates prompts to guide Large Language Models (LLMs) in performing tasks. Existing methods, such as PromptAgent, rely on a single static prompt, which struggles with disjoint cases in complex tasks. Although MoP uses multiple prompts, it fails to account for variations in task complexity. Inspired by programmatic control flow, we introduce a nested if-else structure to address both varying similarities and complexities across diverse cases. We propose Tree-of-Prompts (ToP), which implements this structure by recursively expanding child prompts from a parent prompt. Sibling prompts tackle disjoint cases while inheriting shared similarities from their parent, and handle cases more complex than the parent. Evaluated on Gorilla (understanding), MATH (reasoning), and a subset of BBH benchmarks, ToP outperforms PromptAgent and MoP, with improvements of 1.4% and 4.6% over PromptAgent and 3.2% and 4.5% over MoP, when tested with GPT-4o-mini and Llama 3.2-3B, respectively.
%R 10.18653/v1/2025.findings-acl.995
%U https://aclanthology.org/2025.findings-acl.995/
%U https://doi.org/10.18653/v1/2025.findings-acl.995
%P 19436-19459

Markdown (Informal)

[Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization](https://aclanthology.org/2025.findings-acl.995/) (Kim et al., Findings 2025)

• Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization (Kim et al., Findings 2025)

ACL

• Jihyuk Kim, Shubham Garg, Lahari Poddar, Seung-won Hwang, and Chris Hench. 2025. Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19436–19459, Vienna, Austria. Association for Computational Linguistics.