Fig. 1
Methodological workflow for task-specific feature extraction and refinement of LLMs. The workflow utilizes narrative datasets as input, applying LLM inference to derive comprehensive brain circulation features. The ChatGPT shaped label represents a typical LLM. The conditions in case studies are used to illustrate the significant difference between explicit and implicit circulation in circulation texts, specifically whether the semantics can directly reflect a cross-border circulation behavior with person as the theme, informing the underlying motivations of LLMs. Targeted prompts and responses that meet predefined requirements are then used to augment the task-specific adaptation and reasoning capabilities of LLMs, allowing fine-tuned models to accurately identify entities in narrative texts and establish meaningful connections between them and brain circulation features.
