Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM

• URL: http://arxiv.org/abs/2505.01077v1
• Date: Fri, 02 May 2025 07:33:20 GMT
• Title: Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM
• Authors: Lei Zhao, Ling Kang, Quan Guo,
• Abstract summary: We propose a novel approach to achieve the same results from unannotated full documents using general large language models (LLMs) with lower hardware and labor costs.<n>Our approach combines two major stages: named entity recognition (NER) and relation extraction (RE)<n>To enhance the effectiveness of prompt, we propose a five-part template structure and a scenario-based prompt design principles.
• Score: 7.808231572590279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advent of artificial intelligence (AI), many researchers are attempting to extract structured information from document-level biomedical literature by fine-tuning large language models (LLMs). However, they face significant challenges such as the need for expensive hardware, like high-performance GPUs and the high labor costs associated with annotating training datasets, especially in biomedical realm. Recent research on LLMs, such as GPT-4 and Llama3, has shown promising performance in zero-shot settings, inspiring us to explore a novel approach to achieve the same results from unannotated full documents using general LLMs with lower hardware and labor costs. Our approach combines two major stages: named entity recognition (NER) and relation extraction (RE). NER identifies chemical, disease and gene entities from the document with synonym and hypernym extraction using an LLM with a crafted prompt. RE extracts relations between entities based on predefined relation schemas and prompts. To enhance the effectiveness of prompt, we propose a five-part template structure and a scenario-based prompt design principles, along with evaluation method to systematically assess the prompts. Finally, we evaluated our approach against fine-tuning and pre-trained models on two biomedical datasets: ChemDisGene and CDR. The experimental results indicate that our proposed method can achieve comparable accuracy levels to fine-tuning and pre-trained models but with reduced human and hardware expenses.

Related papers

• Analise Semantica Automatizada com LLM e RAG para Bulas Farmaceuticas [0.0]
  This work investigates the use of RAG (Retrieval-Augmented Generation) architectures combined with Large-Scale Language Models (LLMs) to automate the analysis of documents in PDF format.<n>The proposal integrates vector search techniques by embeddings, semantic data extraction and generation of contextualized natural language responses.
  arXiv  Detail & Related papers  (2025-07-07T17:48:15Z)
• ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data [53.78763789036172]
  We present ChemActor, a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences.<n>This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input.<n>Experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor achieves state-of-the-art performance, outperforming the baseline model by 10%.
  arXiv  Detail & Related papers  (2025-06-30T05:11:19Z)
• Towards Artificial Intelligence Research Assistant for Expert-Involved Learning [64.7438151207189]
  Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research.<n>We present textbfARtificial textbfIntelligence research assistant for textbfExpert-involved textbfLearning (ARIEL)
  arXiv  Detail & Related papers  (2025-05-03T14:21:48Z)
• m-KAILIN: Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training [22.996230737442254]
  Corpus Heading for biomedical large language models (LLMs) seeks to address the pressing challenge of insufficient quantity and quality in open-source scientific corpora.<n>This paper proposes a knowledge-driven, agentic framework for scientific corpus distillation, tailored explicitly for LLM training in the biomedical domain.
  arXiv  Detail & Related papers  (2025-04-28T08:18:24Z)
• Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training [10.701353329227722]
  We propose a framework that automates the distillation of high-quality textual training data from the extensive scientific literature.<n>Our approach self-evaluates and generates questions that are more closely aligned with the biomedical domain.<n>Our approach substantially improves question-answering tasks compared to pre-trained models from the life sciences domain.
  arXiv  Detail & Related papers  (2025-01-25T07:20:44Z)
• NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering [0.14999444543328289]
  We introduce a novel approach that integrates an optimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES optimization technique for enhanced scholarly document abstract categorization. We employ the distilled MiniLM model, fine-tuned on domain-specific data, for high-precision answer extraction.
  arXiv  Detail & Related papers  (2024-10-29T14:45:12Z)
• BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine [19.861178160437827]
  Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains. textscBiomedRAG attains superior performance across 5 biomedical NLP tasks. textscBiomedRAG outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.
  arXiv  Detail & Related papers  (2024-05-01T12:01:39Z)
• An Autonomous Large Language Model Agent for Chemical Literature Data Mining [60.85177362167166]
  We introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data.
  arXiv  Detail & Related papers  (2024-02-20T13:21:46Z)
• Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)<n>Our research aims to transform existing medication recommendation methodologies using LLMs.<n>To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Benchmarking large language models for biomedical natural language processing applications and recommendations [22.668383945059762]
  Large Language Models (LLMs) have shown promise in general domains.<n>We compare their zero-shot, few-shot, and fine-tuning performance with traditional fine-tuning of BERT or BART models.<n>We find issues like missing information and hallucinations in LLM outputs.
  arXiv  Detail & Related papers  (2023-05-10T13:40:06Z)
• Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes [0.0]
  We propose an end-to-end machine learning solution based on large language models (LLMs) The entities used in the KG construction process are diseases, factors, treatments, as well as manifestations that coexist with the patient while experiencing the disease. The application of the proposed methodology is demonstrated on age-related macular degeneration.
  arXiv  Detail & Related papers  (2023-01-29T15:52:33Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.