On the GPQA benchmark, GPT-4o's highest accuracy came from self-consistency prompting, about 53 percent correct, but its explanations were least similar to the reference solutions, while direct answer and chain-of-thought prompts gave the closest explanations.
Can Stories Help LLMs Reason? Curating Information Space Through Narrative
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abstract
Narratives are widely recognized as a powerful tool for structuring information and facilitating comprehension of complex ideas in various domains such as science communication. This paper investigates whether incorporating narrative elements can assist Large Language Models (LLMs) in solving complex problems more effectively. We propose a novel approach, Story of Thought (SoT), integrating narrative structures into prompting techniques for problem-solving. This approach involves constructing narratives around problem statements and creating a framework to identify and organize relevant information. Our experiments show that using various LLMs with SoT consistently surpasses using them with other techniques on physics, chemistry, math, and biology questions in both the GPQA and JEEBench datasets. The narrative-based information curation process in SoT enhances problem comprehension by contextualizing critical in-domain information and highlighting causal relationships within the problem space.
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2025 1verdicts
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Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers
On the GPQA benchmark, GPT-4o's highest accuracy came from self-consistency prompting, about 53 percent correct, but its explanations were least similar to the reference solutions, while direct answer and chain-of-thought prompts gave the closest explanations.