Iterative Graph Alignment

Understand

By compressing diverse narratives, LLMs go beyond memorization, achieving intelligence by capturing generalizable causal relationships.

• However, they suffer from local 'representation gaps' due to insufficient training data diversity, limiting their real-world utility, especially in tasks requiring strict alignment to rules.
• Traditional alignment methods relying on heavy human annotations are inefficient and unscalable.
• Recent self-alignment techniques also fall short, as they often depend on self-selection based prompting and memorization-based learning.