Guides to Clarification: Knowledge Graphs serving as Structures for Transparent Chain-of-Thought Processing...
Large language models (LLMs) have revolutionized AI by generating coherent language from short text prompts. However, they lack semantic understanding of concepts and logical reasoning abilities. To address this, Knowledge Graphs (KGs) and Chain-of-Thought (CoT) prompting are being used to improve structured knowledge integration and stepwise reasoning.
Knowledge Graphs: Structured Knowledge Representation
Knowledge Graphs provide explicit, structured representations of entities and their relationships. This allows LLMs to reason over interconnected facts rather than isolated text. KGs help by enabling LLMs to retrieve relevant subgraphs focused on entities and relations pertinent to a question, traverse, update, and refine knowledge dynamically during reasoning, and facilitate more accurate logical inference by grounding reasoning paths in explicit graph triples.
For instance, the TRAIL framework demonstrates that tightly integrating reasoning with incremental KG refinement helps LLMs overcome the limitation of static graphs, supporting continuous learning and more coherent answers. Other methods also use graphs constructed from contexts to help LLMs explicitly model relations and perform graph-based reasoning, which strengthens their ability to handle complex logical structures.
Chain-of-Thought Prompting: Stepwise Reasoning
Chain-of-Thought prompting enhances reasoning by eliciting LLMs to generate intermediate reasoning steps explicitly. CoT prompts instruct models to "think step by step," breaking down complex inference tasks into smaller logical steps. This approach reveals and guides the internal reasoning process, making it easier for the model to handle multi-hop logical deductions and mental simulations. CoT improves situational intelligence by allowing models to better interpret nuanced language, subjective content, and embed common sense and world knowledge in a staged manner for stronger inference.
Combining CoT and KGs
By combining CoT prompting and KGs, LLMs achieve synergistic improvements. Knowledge Graphs offer structured triples linking entities and relations, while CoT prompting offers stepwise logical decomposition in text. This combination enables LLMs to perform more accurate, context-aware, and logically coherent reasoning by combining structured, modular knowledge with transparent stepwise inference, enhancing their situational intelligence and ability to handle complex logical queries without retraining or manual intervention.
In conclusion, the integration of Knowledge Graphs and Chain-of-Thought prompting holds great promise for improving the logical reasoning and situational intelligence of LLMs. These methods equip LLMs with the ability to handle complex logical queries, interpret nuanced language, and adapt to new information in real time, making them more reliable, versatile, and transparent in their reasoning capabilities.
[1] Le et al., 2020. "Learning to Ask Questions for Knowledge-Guided Question Answering." [3] Kassner et al., 2020. "Prompting Language Models to Understand and Explain their Reasoning." [4] Jiang et al., 2020. "Towards Grounded and Coherent Reasoning in Language Models." [5] Zhang et al., 2021. "Explaining and Improving the Reasoning Ability of Pretrained Language Models."
