Iterative Graph Alignment
<|reference_start|>Iterative Graph Alignment: 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. To address these issues, we introduce Iterative Graph Alignment (IGA), an annotation-free rule-based alignment algorithm. A teacher model (VLM) employs Iterative Graph Prompting (IGP) to create logical graphs and reference answers. The student model (LLM) identifies local knowledge gaps by attempting to align its responses with these references, collaborating with helper models to generate diverse answers. These aligned responses are then used for iterative supervised fine-tuning (SFT). Our evaluations across five rule-based scenarios demonstrate IGP's effectiveness, with a 73.12\% alignment improvement in Claude Sonnet 3.5, and Llama3-8B-Instruct achieving an 86.20\% improvement, outperforming Claude Sonnet 3.5 in rule-based alignment.<|reference_end|>
@article{yu2024iterative,
    title={Iterative Graph Alignment},
    author={Fangyuan Yu, Hardeep Singh Arora, Matt Johnson},
    journal={arXiv preprint arXiv:2408.16667},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16667},
    primaryClass={cs.LG cs.AI cs.CL cs.MA}
}