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PRISM-BN: A Controlled Corpus and Benchmark for Text-to-Parameterized Bayesian Network Extraction — arXiv:2609.21673

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21673
License (as archived): CC0-1.0
Archived: 2026-10-10T01:27:37.047Z
SHA-256 of the archived text: a8eb788f4a11c9727bd4d2728cc6d9dc4aca6a6484f560b8f3d9323fc23706be
Stable URL of this page: https://metraipolis.com/documento/dc_bfdbf1896501

How to cite

PRISM-BN: A Controlled Corpus and Benchmark for Text-to-Parameterized Bayesian Network Extraction — arXiv:2609.21673 — arXiv (en), https://arxiv.org/abs/2609.21673 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bfdbf1896501 (Metraipolis archive dc_bfdbf1896501, sha256 a8eb788f4a11c9727bd4d2728cc6d9dc4aca6a6484f560b8f3d9323fc23706be)

BibTeX

@misc{dcbfdbf1896501,
  title = {PRISM-BN: A Controlled Corpus and Benchmark for Text-to-Parameterized Bayesian Network Extraction — arXiv:2609.21673},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21673},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bfdbf1896501; sha256 a8eb788f4a11c9727bd4d2728cc6d9dc4aca6a6484f560b8f3d9323fc23706be},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21673. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural symbolic targets for neurosymbolic AI. Yet training text-to-parameterized-BN systems requires paired text-to-BN resources unavailable at scale. We introduce PRISM-BN, a controlled corpus of 5054 BN-grounded descriptions paired with discrete reference BNs containing variables, states, directed edges, root priors, and full multi-parent CPDs across five domains. The instances are derived from 50 Wikipedia-seeded backbones, and their probabilities are internally constructed benchmark targets rather than externally validated causal estimates. PRISM-BN is built with PRISM, a marginal-first pipeline that elicits marginal and local joint distributions, analytically recovers normalized CPDs, and constructs locally reparameterized subgraphs. We define a benchmark with semantic node and state alignment, conditional structural scoring, and strict full-CPD evaluation. Across six LLM extractors, Node F1 ranges from 0.56 to 0.83, conditional Edge F1 from 0.90 to 0.97, and CPD-KL from 1.11 to 3.14. Conditional state and edge recovery remain consistently strong, whereas strict full-CPD agreement remains challenging. These trends persist with independently generated GPT-5.5 references, and a human pilot corroborates structural recoverability and similar probabilistic interpretations. PRISM-BN supports separate evaluation of structural recovery and probabilistic parameter estimation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.21673 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T01:00:30.295Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).