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The automatic extraction of biomedical entities and their interaction fromunstructured data remains a challenging task due to the limited availability ofexpert-labeled standard datasets. In this paper, we introduce PETAI-LOR, aretrieval-based language framework that is augmented by tailored chunk scorer.Unlike previous retrieval-augmented language models (LM) that retrieve relevantdocuments by calculating the similarity between the input sentence and thecandidate document set, PETAILOR segments the sentence into chunks andretrieves the relevant chunk from our pre-computed chunk-based relationalkey-value memory. Moreover, in order to comprehend the specific requirements ofthe LM, PETAI-LOR adapt the tailored chunk scorer to the LM. We also introduceGM-CIHT, an expert annotated biomedical triple extraction dataset with morerelation types. This dataset is centered on the non-drug treatment and generalbiomedical domain. Additionally, we investigate the efficacy of tripleextraction models trained on general domains when applied to the biomedicaldomain. Our experiments reveal that PETAI-LOR achieves state-of-the-artperformance on GM-CIHT

PeTailor: Improving Large Language Model by Tailored Chunk Scorer in Biomedical Triple Extraction