IGDA: Interactive Graph Discovery through Large Language Model Agents

• URL: http://arxiv.org/abs/2502.17189v2
• Date: Sun, 13 Apr 2025 16:26:06 GMT
• Title: IGDA: Interactive Graph Discovery through Large Language Model Agents
• Authors: Alex Havrilla, David Alvarez-Melis, Nicolo Fusi,
• Abstract summary: Large language models ($textbfLLMs$) have emerged as a powerful method for discovery.<n>We propose $textbfIGDA$ to be a powerful method for graph discovery complementary to existing numerically driven approaches.
• Score: 6.704529554100875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models ($\textbf{LLMs}$) have emerged as a powerful method for discovery. Instead of utilizing numerical data, LLMs utilize associated variable $\textit{semantic metadata}$ to predict variable relationships. Simultaneously, LLMs demonstrate impressive abilities to act as black-box optimizers when given an objective $f$ and sequence of trials. We study LLMs at the intersection of these two capabilities by applying LLMs to the task of $\textit{interactive graph discovery}$: given a ground truth graph $G^*$ capturing variable relationships and a budget of $I$ edge experiments over $R$ rounds, minimize the distance between the predicted graph $\hat{G}_R$ and $G^*$ at the end of the $R$-th round. To solve this task we propose $\textbf{IGDA}$, a LLM-based pipeline incorporating two key components: 1) an LLM uncertainty-driven method for edge experiment selection 2) a local graph update strategy utilizing binary feedback from experiments to improve predictions for unselected neighboring edges. Experiments on eight different real-world graphs show our approach often outperforms all baselines including a state-of-the-art numerical method for interactive graph discovery. Further, we conduct a rigorous series of ablations dissecting the impact of each pipeline component. Finally, to assess the impact of memorization, we apply our interactive graph discovery strategy to a complex, new (as of July 2024) causal graph on protein transcription factors, finding strong performance in a setting where memorization is impossible. Overall, our results show IGDA to be a powerful method for graph discovery complementary to existing numerically driven approaches.

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