# Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier arXiv: https://arxiv.org/abs/2501.05155 ยท HTML: https://mlsanity.com/paper/2501.05155/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. - Builds on 32 of its 64 references that are in the map. - First posted on arXiv 9 Jan 2025. ## Placed in - Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/) ## Builds on (most cited first, 30 of 32) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020) - [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020) - [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023) - [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022) - [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023) - [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019) - [Is GPT-3 a Good Data Annotator?](https://mlsanity.com/paper/2212.10450/) (2022) - [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021) - [Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling](https://mlsanity.com/paper/2010.11304/) (2020) - [Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents](https://mlsanity.com/paper/2310.19923/) (2023) - [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020) - [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022) - [Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://mlsanity.com/paper/1909.00228/) (2019) - [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021) - [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023) - [RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://mlsanity.com/paper/2203.09101/) (2022) - [Document-Level Relation Extraction with Reconstruction](https://mlsanity.com/paper/2012.11384/) (2020) - [Retrieval-Augmented Code Generation for Universal Information Extraction](https://mlsanity.com/paper/2311.02962/) (2023) - [CodeKGC: Code Language Model for Generative Knowledge Graph Construction](https://mlsanity.com/paper/2304.09048/) (2023) - [Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction](https://mlsanity.com/paper/2401.13598/) (2024) - [BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer](https://mlsanity.com/paper/2101.04158/) (2021) - [Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs](https://mlsanity.com/paper/2105.11225/) (2021) - [Document-level Relation Extraction with Cross-sentence Reasoning Graph](https://mlsanity.com/paper/2303.03912/) (2023) - [Retrieval-Augmented Generation-based Relation Extraction](https://mlsanity.com/paper/2404.13397/) (2024) - [Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction](https://mlsanity.com/paper/2011.13574/) (2020) - [Towards Better Document-level Relation Extraction via Iterative Inference](https://mlsanity.com/paper/2211.14470/) (2022) - [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024) ## Built on by (most cited first, 1 of 1) - [Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?](https://mlsanity.com/paper/2606.15412/) (2026) ## Abstract (arXiv) Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts, serving as a crucial subfield of biomedical text mining. Existing Bio-RE methods struggle with cross-sentence inference, which is essential for capturing relations spanning multiple sentences. Moreover, previous methods often overlook the incompleteness of documents and lack the integration of external knowledge, limiting contextual richness. Besides, the scarcity of annotated data further hampers model training. Recent advancements in large language models (LLMs) have inspired us to explore all the above issues for document-level Bio-RE. Specifically, we propose a document-level Bio-RE framework via LLM Adaptive Document-Relation Cross-Mapping (ADRCM) Fine-Tuning and Concept Unique Identifier (CUI) Retrieval-Augmented Generation (RAG). First, we introduce the Iteration-of-REsummary (IoRs) prompt for solving the data scarcity issue. In this way, Bio-RE task-specific synthetic data can be generated by guiding ChatGPT to focus on entity relations and iteratively refining synthetic data. Next, we propose ADRCM fine-tuning, a novel fine-tuning recipe that establishes mappings across different documents and relations, enhancing the model's contextual understanding and cross-sentence inference capabilities. Finally, during the inference, a biomedical-specific RAG approach, named CUI RAG, is designed to leverage CUIs as indexes for entities, narrowing the retrieval scope and enriching the relevant document contexts. Experiments conducted on three Bio-RE datasets (GDA, CDR, and BioRED) demonstrate the state-of-the-art performance of our proposed method by comparing it with other related works.