Knowledge Graphs are all you need: Leveraging KGs in Physics Question Answering (2412.05453v3)

Abstract: This study explores the effectiveness of using knowledge graphs generated by LLMs to decompose high school-level physics questions into sub-questions. We introduce a pipeline aimed at enhancing model response quality for Question Answering tasks. By employing LLMs to construct knowledge graphs that capture the internal logic of the questions, these graphs then guide the generation of subquestions. We hypothesize that this method yields sub-questions that are more logically consistent with the original questions compared to traditional decomposition techniques. Our results show that sub-questions derived from knowledge graphs exhibit significantly improved fidelity to the original question's logic. This approach not only enhances the learning experience by providing clearer and more contextually appropriate sub-questions but also highlights the potential of LLMs to transform educational methodologies. The findings indicate a promising direction for applying AI to improve the quality and effectiveness of educational content.

• The paper presents a novel method that integrates LLM-generated knowledge graphs to decompose high school physics questions.
• It employs a comprehensive pipeline that generates detailed knowledge graphs to guide the creation of contextually relevant sub-questions.
• Experimental evaluations show that KG-based decomposition improves logical consistency and clarity compared to traditional methods.

Leveraging Knowledge Graphs for Enhanced Physics Question Answering

The paper "Knowledge Graphs are all you need: Leveraging KGs in Physics Question Answering" presents a novel approach to improving the question-answering capabilities of LLMs in the domain of high school-level physics. This paper posits that the integration of knowledge graphs (KGs) generated by LLMs into question decomposition processes can enhance the quality of responses by breaking down complex questions into logically consistent and contextually relevant sub-questions. By doing so, the authors aim to address the challenges posed by the complex, multi-step reasoning often required in educational contexts, particularly in subjects like physics.

The authors introduce a comprehensive pipeline that employs LLMs to generate KGs from original questions, thereby capturing the internal logic and key relationships inherent in the problems. These graphs then inform the generation of sub-questions, which are processed and answered independently before synthesizing a final response to the original query. The authors hypothesize that this KG-based decomposition offers a superior method for preserving the logical structure and intent of the initial question compared to traditional decomposition techniques.

Methodology

In this work, the authors employ LLMs to generate KGs, which serve as structured representations encompassing the entities, relationships, and properties associated with a given question. This formal structure facilitates a deeper understanding of the question, allowing for a more coherent breakdown into sub-questions. The innovation here lies in using these KGs not merely as a storage of facts but as active components that guide the question decomposition process.

A new dataset containing high-quality high school physics questions is introduced, complemented by internal knowledge graphs and subqueries generated by advanced models like Gemini Pro. This dataset serves as a resource to finetune open-source LLMs, enabling them to replicate and potentially surpass the sophisticated analytical capabilities exhibited by larger models.

Experimental and Evaluation Insights

The results indicate that KGs significantly enhance the fidelity and coherence of generated sub-questions, leading to improved answer quality. The paper includes both quantitative and qualitative evaluations, with experiments confirming that KG-enabled decomposition produces answers that adhere more closely to the original question's logic than those generated by traditional methodologies.

In an experimental survey conducted with high school students, the knowledge graph-based method received higher ratings for clarity, logical consistency, and helpfulness compared to standard prompting or question decomposition without KGs. These findings affirm the potential of KGs to not only improve technical accuracy but also optimize the learning process by making complex concepts more digestible.

Limitations and Possible Extensions

While the paper demonstrates substantial qualitative improvements in the context of high school physics, it acknowledges challenges in scalability and domain generalization. The atomization inherent in the approach may lead to higher inference costs due to repetitive information processing. Furthermore, future advancements may be realized by overcoming the hurdles of training data synthesis which could facilitate model finetuning.

The paper underscores the necessity for future research to explore the application of this approach across varied educational domains and question types. Additionally, integrating more sophisticated knowledge graph construction techniques and external knowledge databases could significantly enhance the versatility and scalability of this methodology.

Conclusion

The integration of knowledge graphs into LLM-driven question-answering tasks in physics represents a promising direction for the application of AI in educational methodologies. This research highlights the transformative potential of structured knowledge representation in improving the quality of educational content and personalizing learning experiences. The findings suggest a future where AI systems can facilitate deeper understanding and more robust reasoning in students, enhancing both practical and theoretical educational outcomes.