Papers with Bayesian Networks
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. |
Verifiable Parameterization of Bayesian Networks from Scientific Literature: Unlocking Unstructured Empirical Evidence (2026.findings-acl)
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| Challenge: | Current methods to learn conditional probabilities from raw tabular data are limited due to privacy concerns or general lack of access to data. |
| Approach: | They propose to reconstruct local conditional probability tables solely from statistical summaries to parameterize Bayesian Networks. |
| Outcome: | The proposed methods outperform baseline methods while ensuring transparency and verifiability. |
A Study of Automatic Metrics for the Evaluation of Natural Language Explanations (2021.eacl-main)
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| Challenge: | a lack of transparency is a key issue for robotics and AI. |
| Approach: | They propose to map existing automatic evaluation methods for natural language generation onto explanations. |
| Outcome: | The proposed model shows that embedding-based evaluation methods have higher correlations with human ratings than word-overlap metrics. |
Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs (2020.acl-main)
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| Challenge: | a novel framework for text-based diagnosis of diseases requires appropriate balance between accuracy and interpretability. |
| Approach: | They propose a framework that stacks Bayesian Network Ensembles on top of CNN to build an accurate yet interpretable diagnosis system. |
| Outcome: | The proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable. |
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. |