To enhance LLMs' ability to capture and integrate temporal and causal relationships between events in fictional narratives, the following strategies can be implemented:
Temporal Context Modeling: Develop models that can understand and retain temporal context within narratives to track the sequence of events accurately.
Causal Inference Mechanisms: Incorporate causal inference mechanisms into LLMs to identify cause-effect relationships between events and understand the impact of one event on subsequent occurrences.
Event Dependency Graphs: Construct event dependency graphs to visualize and analyze the relationships between events, enabling LLMs to comprehend the narrative structure better.
Long-Range Dependency Modeling: Enhance LLMs' ability to capture long-range dependencies between events by optimizing architecture and training strategies to handle complex narrative structures.
Contextual Embeddings: Utilize contextual embeddings to encode temporal information and causal links between events, facilitating a more nuanced understanding of character motivations and decisions.
Fine-Tuning on Event Prediction Tasks: Train LLMs on event prediction tasks to improve their ability to predict and infer future events based on past occurrences, enhancing their comprehension of narrative causality.
By implementing these strategies, LLMs can be further improved to capture and integrate temporal and causal relationships between events in fictional narratives, enhancing their performance in tasks like Motivation Recognition and character understanding.