Incorporating dual-system cognitive theory into natural language processing tasks has significant implications for how models approach language generation.
By imitating dual-system cognitive theory, as seen in DECIDER, models can mimic human decision-making processes through an interplay between intuitive System 1 (S1) and logical System 2 (S2). This allows models to make decisions based on both intuition and conscious reasoning, leading to more nuanced and contextually appropriate responses.
Dual-system cognitive theory helps models consider not only immediate goals or targets but also broader strategies or plans for achieving those goals. This can result in more coherent and realistic text generation by guiding models to focus on relevant information beyond just surface-level keywords.
Additionally, incorporating dual-system cognitive theory can enhance model interpretability by providing insights into how decisions are made during language generation tasks. By understanding how S1 and S2 interact within the model, researchers can gain deeper insights into its behavior and potentially improve performance further.