@inproceedings{he-etal-2025-advancing,
    title = "Advancing Reasoning with Off-the-Shelf {LLM}s: A Semantic Structure Perspective",
    author = "He, Pengfei  and
      Li, Zitao  and
      Xing, Yue  and
      Li, Yaliang  and
      Tang, Jiliang  and
      Ding, Bolin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.137/",
    doi = "10.18653/v1/2025.findings-emnlp.137",
    pages = "2538--2566",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Language Models (LLMs) have shown strong capabilities in zero-shot reasoning and generalization to new tasks. However, the zero-shot performance of general LLMs on complex tasks, such as multi-hop reasoning, remains suboptimal, while reasoning LLMs suffer from hallucinations and unfaithfulness. In this paper, to handle these limitations, we introduce a novel structure analysis method that helps LLMs better understand the question structure and guide the problem-solving process. We demonstrate that existing reasoning strategies, such as Chain-of-Thought and ReAct, significantly benefit from the LLM{'}s inherent understanding of semantic structure. We further ground our method in the theory of probabilistic graphical models to support its effectiveness. To enhance the reasoning process, we augment the structure analysis with refinement and retrieval capabilities, forming a multi-agent reasoning system called Structure-oriented Autonomous Reasoning Agents (SARA). Extensive experiments show that SARA significantly improves zero-shot performance on knowledge-intensive and mathematical tasks. Remarkably, our approach makes a general LLM competitive with dedicated reasoning models in several benchmarks and demonstrates strong robustness against corrupted reasoning paths."
}