Papers by Alberto Bugarín-Diz
CausalGraphBench: a Benchmark for Evaluating Language Models capabilities of Causal Graph discovery (2025.acl-srw)
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| Challenge: | Recent advances in large language models (LLMs) have expanded their applications into domains not traditionally associated with natural language processing. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to construct Causal Graphs (CGs) they examine various methods for CG discovery and their performance across different graph sizes and complexity levels. |
| Outcome: | The proposed benchmark comprises 35 CGs sourced from publicly available repositories and academic papers. |
Scalability of Bayesian Network Structure Elicitation with Large Language Models: a Novel Methodology and Comparative Analysis (2025.coling-main)
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| Challenge: | Existing methods for BN structure learning are limited by the size of the BN. |
| Approach: | They propose a method for Bayesian Networks (BNs) structure elicitation that initializes several LLMs with different experiences and queries them to create a structure. |
| Outcome: | The proposed method performs better than the existing method with one of the three studied LLMs, but the performance decreases with the increase in BN size. |