pith:ZZDXTWYH
BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration
BoostTaxo induces taxonomies from domain terms using a boosting-style LLM framework in zero-shot settings.
arxiv:2605.12520 v1 · 2026-04-03 · cs.CL · cs.AI
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Claims
The unified BoostTaxo is evaluated on three public benchmark datasets, namely WordNet, DBLP, and SemEval-Sci, and achieves superior or comparable performance to state-of-the-art methods in zero-shot taxonomy induction.
That the combination of retrieval-augmented definition refinement, hybrid parent candidate selection, and structure-aware score calibration will consistently produce reliable taxonomies without inheriting biases from the underlying LLMs or requiring domain-specific tuning.
BoostTaxo introduces a boosting-style LLM framework for zero-shot taxonomy induction that uses hybrid candidate selection and constraint-aware calibration to achieve superior or comparable performance to prior methods on WordNet, DBLP, and SemEval-Sci benchmarks.
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| First computed | 2026-05-18T03:10:02.874357Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ce4779db078d91e5ab6e0be5ae93ae7a2a52a5ec041bc88f809c8ac02fe1afab
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZZDXTWYHRWI6LK3OBPS25E5OPI \
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Canonical record JSON
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