Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs

• URL: http://arxiv.org/abs/2312.13881v1
• Date: Thu, 21 Dec 2023 14:26:57 GMT
• Title: Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs
• Authors: Juraj Vladika, Alexander Fichtl, Florian Matthes
• Abstract summary: 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.
• Score: 54.223394825528665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in natural language processing (NLP) owe their success to pre-training language models on large amounts of unstructured data. Still, there is an increasing effort to combine the unstructured nature of LMs with structured knowledge and reasoning. Particularly in the rapidly evolving field of biomedical NLP, knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large language models and domain-specific knowledge, considering the available biomedical knowledge graphs (KGs) curated by experts over the decades. In this paper, we develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models (PLMs). We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical ontology OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. The approach includes partitioning knowledge graphs into smaller subgraphs, fine-tuning adapter modules for each subgraph, and combining the knowledge in a fusion layer. We test the performance on three downstream tasks: document classification,question answering, and natural language inference. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low. Finally, we provide a detailed interpretation of the results and report valuable insights for future work.

Related papers

• Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey [75.47055414002571]
  The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. We provide an analysis of recent advancements achieved through cross modeling of biomolecules and natural language.
  arXiv  Detail & Related papers  (2024-03-03T14:59:47Z)
• 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)
• Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling [3.8599767910528917]
  This paper proposes a hybrid approach that integrates the strengths of multiple models. BERT provides contextualized word embeddings, a pre-trained multi-channel CNN for character-level information capture, and following by a BiLSTM + CRF for sequence labelling and modelling dependencies between the words in the text. We evaluate our model on the benchmark i2b2/2010 dataset, achieving an F1-score of 90.11.
  arXiv  Detail & Related papers  (2023-12-24T21:45:36Z)
• Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking [4.8882241537236455]
  This research investigates a method that exploits the in-context learning capabilities of large models for biomedical concept linking. The proposed approach adopts a two-stage retrieve-and-rank framework. It achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7% in chemical entity normalization.
  arXiv  Detail & Related papers  (2023-07-03T16:19:50Z)
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day [85.19963303642427]
  We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics. This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
  arXiv  Detail & Related papers  (2023-06-01T16:50:07Z)
• Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing [55.52858954615655]
  We conduct a systematic study on fine-tuning stability in biomedical NLP. We show that finetuning performance may be sensitive to pretraining settings, especially in low-resource domains. We show that these techniques can substantially improve fine-tuning performance for lowresource biomedical NLP applications.
  arXiv  Detail & Related papers  (2021-12-15T04:20:35Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)
• Improving Biomedical Pretrained Language Models with Knowledge [22.61591249168801]
  We propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases. Specifically, we extract entities from PubMed abstracts and link them to UMLS. We then train a knowledge-aware language model that firstly applies a text-only encoding layer to learn entity representation and applies a text-entity fusion encoding to aggregate entity representation.
  arXiv  Detail & Related papers  (2021-04-21T03:57:26Z)
• 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)
• Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings [8.835844347471626]
  We train several state-of-the-art knowledge graph embedding models on the SNOMED-CT knowledge graph. We make a case for the importance of leveraging the multi-relational nature of knowledge graphs for learning biomedical knowledge representation.
  arXiv  Detail & Related papers  (2020-06-24T14:47:33Z)

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.