Abstract

Recent advances in large language models (LLMs) have enabled their application to non-traditional tasks such as causal graph construction, a key component of reasoning frameworks, including Bayesian Networks. The most effective existing approaches rely on direct prompting, where an LLM generates a complete graph from a full set of variables in a single step. However, the performance of such methods degrades as the number of graph nodes increases. To address this limitation, we explore a divide-and-conquer alternative based on semantic clustering. Node representations are first embedded and clustered, after which subgraphs are constructed independently for each cluster using LLM prompting. The resulting subgraphs are then merged pairwise into a global graph. Contrary to our expectations, this approach leads to a substantial degradation in performance compared to direct prompting baselines, as measured by Structural Hamming Distance (SHD). We attribute this to the misalignment between semantic similarity and causal structure, as well as error propagation during subgraph merging. We report these negative results to highlight the limitations of decomposition strategies in LLM-based causal graphs construction.