Trait-guided detective story generation in seven classic investigative styles with Large Language Models

Detective stories are a particularly popular form of mystery fiction driven by investigative reasoning and the gradual revelation of hidden truths. This work presents a trait-guided method for generating detective stories that reflect the investigative styles of classic fictional detectives while maintaining narrative quality. Building upon a preliminary study that characterized the investigative methods of seven iconic detectives (Hercule Poirot, Sherlock Holmes, William Murdoch, Columbo, Father Brown, Miss Marple, and Auguste Dupin), we developed a new narrative generation approach that uses formalized investigative trait profiles and Large Language Models (LLMs) to write detective stories. The method integrates these profiles with narrative principles grounded in literary theory, particularly Todorov’s dual narrative structure, to produce stories where selected detectives solve crimes according to their characteristic investigative approaches. We conducted a two-phase evaluation study with 210 trait-guided stories to assess both the recognizability of detective-specific investigative styles and the overall narrative quality of the generated stories. In Phase 1, state-of-the-art language models achieved 82.4% accuracy in identifying detectives from anonymized narratives based on investigative characteristics, with 78.8% accuracy when restricted to investigative methods alone, demonstrating that the generated stories successfully embedded recognizable investigative styles. In Phase 2, LLM-based quality evaluation revealed that trait-guided stories achieved higher scores than stories generated without trait guidance on empathy, coherence, surprise, and complexity, with length-controlled analyses confirming robust condition effects on empathy and coherence, and particularly strong improvements for psychologically-oriented detectives. A preliminary human validation supported the central empathy finding and indicated that the trait-guided method’s benefits on surprise and engagement may be more pronounced than the LLM scores alone suggest. These findings demonstrate that computational methods can generate detective narratives that preserve distinctive investigative styles while enhancing character depth and emotional portrayal, contributing to the field of computational narratology and AI-assisted creative writing.