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    <abstract>Retrieving the biological impacts of protein-protein interactions (PPIs) is essential for target identification (Target ID) in drug development. Given the vast number of proteins involved, this process remains time-consuming and challenging. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks have supported Target ID; however, no benchmark currently exists for identifying the biological impacts of PPIs. To bridge this gap, we introduce the RAG Benchmark for PPIs (RAGPPI), a factual question-answer benchmark of 4,420 question-answer pairs that focus on the potential biological impacts of PPIs. Through interviews with experts, we identified criteria for a benchmark dataset, such as a type of QA and source. We built a gold-standard dataset (500 QA pairs) through expert-driven data annotation. We developed an ensemble auto-evaluation LLM that incorporates expert labeling characteristics, average fact–abstract similarity (F₁), and low-similarity fact counts (F₂), enabling the construction of a silver-standard dataset (3,720 QA pairs). We are committed to maintaining RAGPPI as a resource to support the research community in advancing RAG systems for drug discovery QA solutions.</abstract>
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%0 Conference Proceedings
%T RAGPPI: Retrieval-Augmented Generation Benchmark for Protein–Protein Interactions in Drug Discovery
%A Jeon, Youngseung
%A Li, Ziwen
%A Li, Thomas
%A Chang, JiaSyuan
%A Ziyadi, Morteza
%A Chen, Xiang Anthony
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F jeon-etal-2026-ragppi
%X Retrieving the biological impacts of protein-protein interactions (PPIs) is essential for target identification (Target ID) in drug development. Given the vast number of proteins involved, this process remains time-consuming and challenging. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks have supported Target ID; however, no benchmark currently exists for identifying the biological impacts of PPIs. To bridge this gap, we introduce the RAG Benchmark for PPIs (RAGPPI), a factual question-answer benchmark of 4,420 question-answer pairs that focus on the potential biological impacts of PPIs. Through interviews with experts, we identified criteria for a benchmark dataset, such as a type of QA and source. We built a gold-standard dataset (500 QA pairs) through expert-driven data annotation. We developed an ensemble auto-evaluation LLM that incorporates expert labeling characteristics, average fact–abstract similarity (F₁), and low-similarity fact counts (F₂), enabling the construction of a silver-standard dataset (3,720 QA pairs). We are committed to maintaining RAGPPI as a resource to support the research community in advancing RAG systems for drug discovery QA solutions.
%U https://aclanthology.org/2026.eacl-long.203/
%P 4345-4363

Markdown (Informal)

[RAGPPI: Retrieval-Augmented Generation Benchmark for Protein–Protein Interactions in Drug Discovery](https://aclanthology.org/2026.eacl-long.203/) (Jeon et al., EACL 2026)

• RAGPPI: Retrieval-Augmented Generation Benchmark for Protein–Protein Interactions in Drug Discovery (Jeon et al., EACL 2026)

ACL

• Youngseung Jeon, Ziwen Li, Thomas Li, JiaSyuan Chang, Morteza Ziyadi, and Xiang Anthony Chen. 2026. RAGPPI: Retrieval-Augmented Generation Benchmark for Protein–Protein Interactions in Drug Discovery. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4345–4363, Rabat, Morocco. Association for Computational Linguistics.