@inproceedings{sadiri-javadi-etal-2025-stories,
    title = "Can Stories Help {LLM}s Reason? Curating Information Space Through Narrative",
    author = "Sadiri Javadi, Vahid  and
      Trippas, Johanne  and
      Lal, Yash Kumar  and
      Flek, Lucie",
    editor = "Rambelli, Giulia  and
      Ilievski, Filip  and
      Bolognesi, Marianna  and
      Sommerauer, Pia",
    booktitle = "Proceedings of the 2nd Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-Angle II)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.analogyangle-1.8/",
    pages = "92--107",
    ISBN = "979-8-89176-274-9",
    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 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."
}