Advancing Reasoning in Large Language Models: Promising Methods and Approaches

2025-02-05

AI Summary

Large language models (LLMs) excel at fluent text generation and factual recall but remain substantially outperformed by humans on complex reasoning tasks—including logical deduction, mathematical computation, commonsense inference, and multi-step reasoning. This paper systematically surveys state-of-the-art approaches to enhance LLM reasoning capabilities. We unify and categorize three major technical directions: (1) prompt engineering techniques (e.g., Chain-of-Thought, Tree-of-Thought), (2) architectural innovations (e.g., retrieval-augmented generation, modular reasoning networks, neuro-symbolic integration), and (3) learning paradigms (e.g., reasoning-specific fine-tuning, reinforcement learning). We propose the most comprehensive taxonomy of LLM reasoning methods to date, explicitly identifying critical evaluation bottlenecks and generalization limitations. Our analysis highlights hallucination mitigation, robustness enhancement, and cross-task generalization as pivotal avenues for future breakthroughs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) have succeeded remarkably in various natural language processing (NLP) tasks, yet their reasoning capabilities remain a fundamental challenge. While LLMs exhibit impressive fluency and factual recall, their ability to perform complex reasoning-spanning logical deduction, mathematical problem-solving, commonsense inference, and multi-step reasoning-often falls short of human expectations. This survey provides a comprehensive review of emerging techniques enhancing reasoning in LLMs. We categorize existing methods into key approaches, including prompting strategies (e.g., Chain-of-Thought reasoning, Self-Consistency, and Tree-of-Thought reasoning), architectural innovations (e.g., retrieval-augmented models, modular reasoning networks, and neuro-symbolic integration), and learning paradigms (e.g., fine-tuning with reasoning-specific datasets, reinforcement learning, and self-supervised reasoning objectives). Additionally, we explore evaluation frameworks used to assess reasoning in LLMs and highlight open challenges, such as hallucinations, robustness, and reasoning generalization across diverse tasks. By synthesizing recent advancements, this survey aims to provide insights into promising directions for future research and practical applications of reasoning-augmented LLMs.

Problem

• Enhancing reasoning in Large Language Models
• Addressing limitations in complex reasoning tasks
• Exploring methods to improve reasoning generalization

Innovation

• Chain-of-Thought prompting
• Retrieval-augmented architectures
• Reasoning-specific dataset fine-tuning