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        <title>Reasoning vs. Validation in LLM-based Pharmacological Parameter Extraction</title>
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            <namePart type="given">Pepa</namePart>
            <namePart type="family">Atanasova</namePart>
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            <namePart type="given">Minghan</namePart>
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    <abstract>Pharmacokinetic (PK) parameters are critical to drug development, but many remain embedded in free-text biomedical literature rather than a structured form. Existing extraction approaches primarily target explicit parameter mentions, while a substantial proportion of PK values must be inferred from context. We introduce implicit PK parameter extraction, where parameter identity is not explicitly stated but must be resolved through domain-grounded, multi-sentence reasoning. To address this challenge, we propose CORVA, a validation-enhanced LLM-based extraction framework that integrates reasoning and domain constraints to extract structured PK tuples from text. We also construct a labelled dataset from DrugBank absorption descriptions, of which 47.5% involve implicit expressions. Relative to the strongest baseline, the proposed method improves F1 by 35.3% and tuple accuracy by 52.9%. CORVA achieves the highest implicit recognition rate at 45.8%, which is 10.5 points ahead of the runner-up. Our results demonstrate that reliable implicit PK extraction requires validation as a first-class component rather than a post-processing step, which offers a practical framework for scalable knowledge extraction from biomedical literature. The code and data are available at https://github.com/wuqinhao/CORVA.</abstract>
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%0 Conference Proceedings
%T Reasoning vs. Validation in LLM-based Pharmacological Parameter Extraction
%A Wu, Qinhao
%A Cui, Xia
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F wu-cui-2026-reasoning
%X Pharmacokinetic (PK) parameters are critical to drug development, but many remain embedded in free-text biomedical literature rather than a structured form. Existing extraction approaches primarily target explicit parameter mentions, while a substantial proportion of PK values must be inferred from context. We introduce implicit PK parameter extraction, where parameter identity is not explicitly stated but must be resolved through domain-grounded, multi-sentence reasoning. To address this challenge, we propose CORVA, a validation-enhanced LLM-based extraction framework that integrates reasoning and domain constraints to extract structured PK tuples from text. We also construct a labelled dataset from DrugBank absorption descriptions, of which 47.5% involve implicit expressions. Relative to the strongest baseline, the proposed method improves F1 by 35.3% and tuple accuracy by 52.9%. CORVA achieves the highest implicit recognition rate at 45.8%, which is 10.5 points ahead of the runner-up. Our results demonstrate that reliable implicit PK extraction requires validation as a first-class component rather than a post-processing step, which offers a practical framework for scalable knowledge extraction from biomedical literature. The code and data are available at https://github.com/wuqinhao/CORVA.
%U https://aclanthology.org/2026.emnlp-industry.83/
%P 1403-1417

Markdown (Informal)

[Reasoning vs. Validation in LLM-based Pharmacological Parameter Extraction](https://aclanthology.org/2026.emnlp-industry.83/) (Wu & Cui, EMNLP 2026)

• Reasoning vs. Validation in LLM-based Pharmacological Parameter Extraction (Wu & Cui, EMNLP 2026)

ACL

• Qinhao Wu and Xia Cui. 2026. Reasoning vs. Validation in LLM-based Pharmacological Parameter Extraction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1403–1417, Budapest, Hungary. Association for Computational Linguistics.