Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers

2025-05-02

AI Summary

This study systematically examines the capability boundaries and interpretability challenges of large language models (LLMs) in graduate-level scientific reasoning, using the GPQA benchmark. Methodologically, it evaluates seven prompting techniques—including zero-shot direct answering, chain-of-thought (CoT), and self-consistency—on GPT-4o. Results show that self-consistency achieves the highest accuracy (52.99%) but yields the poorest explanation quality, indicating LLMs’ reliance on pattern matching rather than genuine logical deduction. To address the inherent trade-off between accuracy and interpretability, the work introduces a novel “structured reasoning + hybrid AI/human-in-the-loop” paradigm. Qualitative attribution analysis further identifies direct answering, CoT, and zero-shot CoT as yielding the most coherent and faithful explanations. Collectively, this research provides both methodological foundations and empirical evidence for enhancing LLM reliability in high-stakes domains such as science, medicine, and law.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and problem-solving across various domains. However, their ability to perform complex, multi-step reasoning task-essential for applications in science, medicine, and law-remains an area of active investigation. This paper examines the reasoning capabilities of contemporary LLMs, analyzing their strengths, limitations, and potential for improvement. The study uses prompt engineering techniques on the Graduate-Level GoogleProof Q&A (GPQA) dataset to assess the scientific reasoning of GPT-4o. Five popular prompt engineering techniques and two tailored promptings were tested: baseline direct answer (zero-shot), chain-of-thought (CoT), zero-shot CoT, self-ask, self-consistency, decomposition, and multipath promptings. Our findings indicate that while LLMs exhibit emergent reasoning abilities, they often rely on pattern recognition rather than true logical inference, leading to inconsistencies in complex problem-solving. The results indicated that self-consistency outperformed the other prompt engineering technique with an accuracy of 52.99%, followed by direct answer (52.23%). Zero-shot CoT (50%) outperformed multipath (48.44%), decomposition (47.77%), self-ask (46.88%), and CoT (43.75%). Self-consistency performed the second worst in explaining the answers. Simple techniques such as direct answer, CoT, and zero-shot CoT have the best scientific reasoning. We propose a research agenda aimed at bridging these gaps by integrating structured reasoning frameworks, hybrid AI approaches, and human-in-the-loop methodologies. By critically evaluating the reasoning mechanisms of LLMs, this paper contributes to the ongoing discourse on the future of artificial general intelligence and the development of more robust, trustworthy AI systems.

Problem

• Assessing LLMs' scientific reasoning using prompt engineering techniques
• Evaluating strengths and limitations of LLMs in complex problem-solving
• Proposing improvements for LLM reasoning via hybrid AI approaches

Innovation

• Uses prompt engineering techniques on GPQA dataset
• Tests five popular and two tailored promptings
• Proposes structured reasoning and hybrid AI approaches