Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation
This addresses the issue of cognitive misalignment in LLMs for users with differing cognitive abilities, offering a domain-specific solution.
The paper tackled the problem of large language models struggling to adapt content to users with varying cognitive capacities, termed cognitive misalignment, and proposed the Cognitive-Level Alignment Framework (CLAF) to align knowledge complexity and presentation style, resulting in enhanced adaptability and informativeness of outputs across user profiles.
Large Language Models (LLMs) have demonstrated strong performance in open-ended generation tasks. However, they often struggle to adapt content to users with differing cognitive capacities, leading to a phenomenon we term cognitive misalignment. This issue arises in two forms: knowledge-level misalignment, where content is too complex or too simplistic relative to user understanding, and presentation-style misalignment, where the structure or tone hinders effective comprehension. To address these challenges, we propose the Cognitive-Level Alignment Framework (CLAF), a general-purpose generation framework that aligns both knowledge complexity and presentation style with user cognition. CLAF integrates a capability-aware retrieval module based on a hierarchical knowledge graph and a style optimization module guided by Bloom's taxonomy and preference learning. Additionally, a knowledge-controllable generation component ensures consistency and relevance throughout the output. To support training and evaluation, we construct SCALE, a cognitively annotated dataset containing responses at multiple comprehension levels per query. Empirical results show that CLAF enhances the adaptability and informativeness of LLM outputs across a range of user profiles, offering a robust solution to cognitive-level alignment in real-world applications.