Can Language Models be Biomedical Knowledge Bases?

• URL: http://arxiv.org/abs/2109.07154v1
• Date: Wed, 15 Sep 2021 08:34:56 GMT
• Title: Can Language Models be Biomedical Knowledge Bases?
• Authors: Mujeen Sung, Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, Jaewoo Kang
• Abstract summary: We create the BioLAMA benchmark comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge.
• Score: 18.28724653601921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs contain and how we can extract that knowledge, treating LMs as knowledge bases (KBs). While there has been much work on probing LMs in the general domain, there has been little attention to whether these powerful LMs can be used as domain-specific KBs. To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge. Although this seems promising given the task difficulty, our detailed analyses reveal that most predictions are highly correlated with prompt templates without any subjects, hence producing similar results on each relation and hindering their capabilities to be used as domain-specific KBs. We hope that BioLAMA can serve as a challenging benchmark for biomedical factual probing.

Related papers

• BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text [82.7001841679981]
  BioMedLM is a 2.7 billion parameter GPT-style autoregressive model trained exclusively on PubMed abstracts and full articles. When fine-tuned, BioMedLM can produce strong multiple-choice biomedical question-answering results competitive with larger models. BioMedLM can also be fine-tuned to produce useful answers to patient questions on medical topics.
  arXiv  Detail & Related papers  (2024-03-27T10:18:21Z)
• 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)
• How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction? [1.7555695340815782]
  General-domain models typically outperformed biomedical-domain models. biomedical instruction finetuning improved performance to a similar degree as general instruction finetuning. Our findings suggest it may be more fruitful to focus research effort on larger-scale biomedical instruction finetuning of general LMs.
  arXiv  Detail & Related papers  (2024-02-21T01:57:58Z)
• 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)
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining [140.61707108174247]
  We propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA.
  arXiv  Detail & Related papers  (2022-10-19T07:17:39Z)
• Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context [7.7971830917251275]
  We use prompt methods to extract knowledge from Language Models (LMs) as new knowledge Bases (LMs as KBs) We specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain. Our experiments show that the knowledge possessed by those language models can distinguish the correct knowledge from the noise knowledge in the EHR notes.
  arXiv  Detail & Related papers  (2022-08-26T00:01:26Z)
• Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models [16.535312449449165]
  We release a well-curated biomedical knowledge probing benchmark, MedLAMA, based on the Unified Medical Language System (UMLS) Metathesaurus. We test a wide spectrum of state-of-the-art PLMs and probing approaches on our benchmark, reaching at most 3% of acc@10. We propose Contrastive-Probe, a novel self-supervised contrastive probing approach, that adjusts the underlying PLMs without using any probing data.
  arXiv  Detail & Related papers  (2021-10-15T16:00:11Z)
• 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)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• 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)

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.