End-to-End DAG-Path prompting (EEDP), is a graph flattening technique designed to improve reasoning performance in Large Language Models (LLMs), particularly in scenarios involving long-range dependencies in graphs.
Graphs are often used to represent relationships in data, such as in social networks or molecular structures, but LLMs cannot handle graphs directly. Graph flattening converts graphs into textual formats so that LLMs can process them.
The challenge with existing methods is that they perform well for short-distance reasoning but struggle with long-distance scenarios. Inspired by how humans reason about complex structures, EEDP optimizes graph flattening by focusing on the most important paths in a graph, improving LLMs' ability to understand long-range relationships without losing performance in short-distance scenarios.
Compared to traditional graph-flattening methods like adjacency lists or edge lists, EEDP introduces several innovations:
EEDP was tested on two datasets: Merged 1000 (from educational knowledge graphs) and ZINC test 2500 (molecular graphs). The method outperformed traditional graph-flattening approaches on tasks such as connectivity prediction (whether two nodes are connected) and distance prediction (length of the path between nodes).
Dataset | Task | Accuracy Improvement (%) |
---|---|---|
Merged 1000 | Connectivity Prediction | +7% |
ZINC test 2500 | Distance Prediction | +10% |
The method showed particular strength in handling long-distance relationships, where previous techniques often struggled, making it highly effective for more complex graph-based tasks.
EEDP provides a robust and efficient way to flatten graphs for LLMs, enabling these models to perform well in reasoning tasks that involve both short- and long-distance dependencies in graph structures. By mimicking human cognitive reasoning and optimizing graph descriptions, EEDP enhances the performance of LLMs on complex graph-based tasks.
Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.