Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training

• URL: http://arxiv.org/abs/2501.15108v1
• Date: Sat, 25 Jan 2025 07:20:44 GMT
• Title: Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training
• Authors: Xunxin Cai, Chengrui Wang, Qingqing Long, Yuanchun Zhou, Meng Xiao,
• Abstract summary: We propose a framework that automates the distillation of high-quality textual training data from the extensive scientific literature. Our approach self-evaluates and generates questions that are more closely aligned with the biomedical domain. Our approach substantially improves question-answering tasks compared to pre-trained models from the life sciences domain.
• Score: 10.701353329227722
• License:
• Abstract: The rapid advancement of large language models (LLMs) in biological-medical applications has highlighted a gap between their potential and the limited scale and often low quality of available open-source annotated textual datasets. In addition, the inherent complexity of the biomedical knowledge hierarchy significantly hampers efforts to bridge this gap.Can LLMs themselves play a pivotal role in overcoming this limitation? Motivated by this question, we investigate this challenge in the present study.We propose a framework that automates the distillation of high-quality textual training data from the extensive scientific literature. Our approach self-evaluates and generates questions that are more closely aligned with the biomedical domain, guided by the biomedical knowledge hierarchy through medical subject headings (MeSH). This comprehensive framework establishes an automated workflow, thereby eliminating the need for manual intervention. Furthermore, we conducted comprehensive experiments to evaluate the impact of our framework-generated data on downstream language models of varying sizes. Our approach substantially improves question-answering tasks compared to pre-trained models from the life sciences domain and powerful close-source models represented by GPT-4. Notably, the generated AI-Ready dataset enabled the Llama3-70B base model to outperform GPT-4 using MedPrompt with multiple times the number of parameters. Detailed case studies and ablation experiments underscore the significance of each component within our framework

Related papers

• Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models [51.316001071698224]
  We introduce Biology-Instructions, the first large-scale multi-omics biological sequences-related instruction-tuning dataset. This dataset can bridge the gap between large language models (LLMs) and complex biological sequences-related tasks. We also develop a strong baseline called ChatMultiOmics with a novel three-stage training pipeline.
  arXiv  Detail & Related papers  (2024-12-26T12:12:23Z)
• 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)
• Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
  We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• A Survey for Large Language Models in Biomedicine [31.719451674137844]
  This review is based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv. We explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine. We discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics.
  arXiv  Detail & Related papers  (2024-08-29T12:39:16Z)
• LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction [13.965777046473885]
  Large Language Models (LLMs) are increasingly adopted for applications in healthcare. It is unclear how well LLMs perform on tasks that are traditionally pursued in the biomedical domain.
  arXiv  Detail & Related papers  (2024-08-22T09:37:40Z)
• GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI [67.09501109871351]
  Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals. GMAI-MMBench is the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format.
  arXiv  Detail & Related papers  (2024-08-06T17:59:21Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• 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)
• A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations [22.668383945059762]
  We present a systematic evaluation of four representative Large Language Models (LLMs) across 12 BioNLP datasets. The evaluation is conducted under four settings: zero-shot, static few-shot, dynamic K-nearest few-shot, and fine-tuning. We compare these models against state-of-the-art (SOTA) approaches that fine-tune (domain-specific) BERT or BART models.
  arXiv  Detail & Related papers  (2023-05-10T13:40:06Z)
• Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies [25.990479833023166]
  Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature. We propose a simple yet unexplored approach, which we call biomedical entity-aware masking (BEM) We encourage masked language models to learn entity-centric knowledge based on the pivotal entities characterizing the domain at hand, and employ those entities to drive the LM fine-tuning. Experimental results show performance on par with state-of-the-art models on several biomedical QA datasets.
  arXiv  Detail & Related papers  (2021-02-16T18:51:13Z)
• 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.