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    <abstract>Large language models (LLMs) excel in natural language tasks, with Chain-of-Thought (CoT) prompting enhancing reasoning through step-by-step decomposition. However, CoT struggles in knowledge-intensive tasks with multiple entities and implicit multi-hop relations, failing to connect entities systematically in zero-shot settings. Existing knowledge graph methods, limited by static structures, lack adaptability in complex scenarios. We propose DS-MHP, a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. DS-MHP operates in three stages: 1) constructing query-specific subgraphs by extracting entities and relations; 2) generating and refining multi-hop paths using a hybrid strategy of Breadth-First Search, greedy expansion, and LLM supplementation; and 3) guiding LLMs with subgraphs and paths, aggregating answers via majority voting. Evaluated on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning, DS-MHP outperforms baselines and state-of-the-art methods in nearly all benchmarks. It achieves overall average accuracy increases of 3.9% on Mistral-7B and 3.6% on GPT-3.5 Turbo compared to SOTA, with significant gains in logical and symbolic reasoning. Additionally, DS-MHP reduces runtime and LLM calls compared to SOTA, enhancing computational efficiency. These improvements demonstrate DS-MHP’s superior reasoning accuracy, explainability, and efficiency in complex multi-entity tasks.</abstract>
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%0 Conference Proceedings
%T DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path
%A Liu, Yongqiang
%A Peng, Qiyao
%A Liu, Binrong
%A Liu, Hongtao
%A Li, XueWei
%A Wang, Wenjun
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-ds
%X Large language models (LLMs) excel in natural language tasks, with Chain-of-Thought (CoT) prompting enhancing reasoning through step-by-step decomposition. However, CoT struggles in knowledge-intensive tasks with multiple entities and implicit multi-hop relations, failing to connect entities systematically in zero-shot settings. Existing knowledge graph methods, limited by static structures, lack adaptability in complex scenarios. We propose DS-MHP, a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. DS-MHP operates in three stages: 1) constructing query-specific subgraphs by extracting entities and relations; 2) generating and refining multi-hop paths using a hybrid strategy of Breadth-First Search, greedy expansion, and LLM supplementation; and 3) guiding LLMs with subgraphs and paths, aggregating answers via majority voting. Evaluated on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning, DS-MHP outperforms baselines and state-of-the-art methods in nearly all benchmarks. It achieves overall average accuracy increases of 3.9% on Mistral-7B and 3.6% on GPT-3.5 Turbo compared to SOTA, with significant gains in logical and symbolic reasoning. Additionally, DS-MHP reduces runtime and LLM calls compared to SOTA, enhancing computational efficiency. These improvements demonstrate DS-MHP’s superior reasoning accuracy, explainability, and efficiency in complex multi-entity tasks.
%R 10.18653/v1/2025.findings-emnlp.600
%U https://aclanthology.org/2025.findings-emnlp.600/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.600
%P 11216-11230

Markdown (Informal)

[DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path](https://aclanthology.org/2025.findings-emnlp.600/) (Liu et al., Findings 2025)

• DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path (Liu et al., Findings 2025)

ACL

• Yongqiang Liu, Qiyao Peng, Binrong Liu, Hongtao Liu, XueWei Li, and Wenjun Wang. 2025. DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 11216–11230, Suzhou, China. Association for Computational Linguistics.