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        <title>CrossDDI: Cross-Source Evidence-Grounded Drug-Drug Interaction Verification</title>
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    <abstract>LLM-based drug–drug interaction (DDI) assessment remains difficult to audit when predictions are not explicitly tied to evidence. While retrieval-augmented generation (RAG) improves grounding, predictions are not guaranteed to be entailed by retrieved items. We present CrossDDI, a verification-first framework that separates LLM-based evidence extraction from deterministic, LLM-free arbitration over DrugBank and PubMed, requiring positive predictions to be linked to explicit supporting evidence. Evaluated on 1,000 DDInter 2.0 pairs under a positive–unlabeled setting, CrossDDI achieves recall of 0.576–0.593 over confirmed positives with interaction prediction rates comparable to RAG, while reducing cross-backbone variation (0.018 vs. 0.066). Analysis identifies literature evidence acquisition and attribution as the primary bottleneck: PubMed retrieval covers only 40.5% of confirmed positives, and Path B-only evidence is substantially less reliable than structured evidence. These results suggest that verification-first architectures can improve traceability and backbone consistency, while broader and more reliable literature evidence is needed to extend coverage beyond structured sources.</abstract>
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%0 Conference Proceedings
%T CrossDDI: Cross-Source Evidence-Grounded Drug-Drug Interaction Verification
%A Chu, Bohao
%A Fuhr, Norbert
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S BioNLP 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-434-7
%F chu-fuhr-2026-crossddi
%X LLM-based drug–drug interaction (DDI) assessment remains difficult to audit when predictions are not explicitly tied to evidence. While retrieval-augmented generation (RAG) improves grounding, predictions are not guaranteed to be entailed by retrieved items. We present CrossDDI, a verification-first framework that separates LLM-based evidence extraction from deterministic, LLM-free arbitration over DrugBank and PubMed, requiring positive predictions to be linked to explicit supporting evidence. Evaluated on 1,000 DDInter 2.0 pairs under a positive–unlabeled setting, CrossDDI achieves recall of 0.576–0.593 over confirmed positives with interaction prediction rates comparable to RAG, while reducing cross-backbone variation (0.018 vs. 0.066). Analysis identifies literature evidence acquisition and attribution as the primary bottleneck: PubMed retrieval covers only 40.5% of confirmed positives, and Path B-only evidence is substantially less reliable than structured evidence. These results suggest that verification-first architectures can improve traceability and backbone consistency, while broader and more reliable literature evidence is needed to extend coverage beyond structured sources.
%U https://aclanthology.org/2026.bionlp-1.73/
%P 911-919

Markdown (Informal)

[CrossDDI: Cross-Source Evidence-Grounded Drug-Drug Interaction Verification](https://aclanthology.org/2026.bionlp-1.73/) (Chu & Fuhr, BioNLP 2026)

• CrossDDI: Cross-Source Evidence-Grounded Drug-Drug Interaction Verification (Chu & Fuhr, BioNLP 2026)

ACL

• Bohao Chu and Norbert Fuhr. 2026. CrossDDI: Cross-Source Evidence-Grounded Drug-Drug Interaction Verification. In BioNLP 2026, pages 911–919, San Diego, California. Association for Computational Linguistics.