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 explores whether generating narratives can serve “as a specialized mode of thinking” that improves the reasoning abilities of Large Language Models (LLMs). We introduce Story of Thought (SoT), a novel prompt-driven reasoning framework that guides LLMs to construct narratives around the problem statement to solve the task more effectively. SoT enables LLMs to integrate narrative techniques such as metaphor and analogy into their reasoning process. Our experiments show that SoT significantly improves the LLMs’ problem-solving abilities on various tasks, including physics, chemistry, and biology in both JEEBench and GPQA (e.g., SoT resulted in 13% improvement compared to CoT when using GPT-4). To validate LLM-based evaluation for generated narratives, we conduct a human annotation of the narrative techniques used by LLMs. Our results show strong inter-annotator agreement between Llama 3 70B and human annotators. This work brings LLM reasoning closer to human cognitive processes by mirroring mechanisms such as analogical problem-solving, which are central to how humans understand and process complex ideas.

When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations is complicated by the fact that language models do not possess authentic opinions for their lack of real-world experience. We address this problem by leveraging product reviews as a rich source of product opinions to ground conversational AI in true subjective narratives. With OpinionConv, we develop the first conversational AI for simulating sales conversations. To validate the generated conversations, we conduct several user studies showing that the generated opinions are perceived as realistic. Our assessors also confirm the importance of opinions as an informative basis for decision making.