Advancing Reasoning with Off-the-Shelf LLMs: A Semantic Structure Perspective

ACL ARR 2025 May Submission4389 Authors

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 **S**tructure-oriented **A**utonomous **R**easoning **A**gents (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.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: large language model, reasoning, agent

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Keywords: large language model, reasoning, LLM agent

Submission Number: 4389