BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning

• URL: http://arxiv.org/abs/2410.18955v1
• Date: Thu, 24 Oct 2024 17:53:53 GMT
• Title: BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning
• Authors: Yujuan Velvin Fu, Giridhar Kaushik Ramachandran, Namu Park, Kevin Lybarger, Fei Xia, Ozlem Uzuner, Meliha Yetisgen,
• Abstract summary: We develop BioMistral-NLU, a generalizable medical NLU model, through fine-tuning BioMistral on MNLU-Instruct. Our experiments show that our BioMistral-NLU outperforms the original BioMistral. Our dataset-agnostic prompting strategy and instruction tuning step over diverse NLU tasks enhance LLMs' generalizability across diverse medical NLU tasks.
• Score: 19.027921909970832
• License:
• Abstract: Large language models (LLMs) such as ChatGPT are fine-tuned on large and diverse instruction-following corpora, and can generalize to new tasks. However, those instruction-tuned LLMs often perform poorly in specialized medical natural language understanding (NLU) tasks that require domain knowledge, granular text comprehension, and structured data extraction. To bridge the gap, we: (1) propose a unified prompting format for 7 important NLU tasks, % through span extraction and multi-choice question-answering (QA), (2) curate an instruction-tuning dataset, MNLU-Instruct, utilizing diverse existing open-source medical NLU corpora, and (3) develop BioMistral-NLU, a generalizable medical NLU model, through fine-tuning BioMistral on MNLU-Instruct. We evaluate BioMistral-NLU in a zero-shot setting, across 6 important NLU tasks, from two widely adopted medical NLU benchmarks: Biomedical Language Understanding Evaluation (BLUE) and Biomedical Language Understanding and Reasoning Benchmark (BLURB). Our experiments show that our BioMistral-NLU outperforms the original BioMistral, as well as the proprietary LLMs - ChatGPT and GPT-4. Our dataset-agnostic prompting strategy and instruction tuning step over diverse NLU tasks enhance LLMs' generalizability across diverse medical NLU tasks. Our ablation experiments show that instruction-tuning on a wider variety of tasks, even when the total number of training instances remains constant, enhances downstream zero-shot generalization.

Related papers

• Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding [92.32881381717594]
  We introduce ALternate Contrastive Decoding (ALCD) to solve hallucination issues in medical information extraction tasks. ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.
  arXiv  Detail & Related papers  (2024-10-21T07:19:19Z)
• Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development [24.5979645373074]
  Y-Mol is a knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. It learns from a corpus of publications, knowledge graphs, and expert-designed synthetic data. Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events.
  arXiv  Detail & Related papers  (2024-10-15T12:39:20Z)
• 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)
• When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models [59.84769254832941]
  We propose a FaLlacy Understanding Benchmark (FLUB) containing cunning texts that are easy for humans to understand but difficult for models to grasp. Specifically, the cunning texts that FLUB focuses on mainly consist of the tricky, humorous, and misleading texts collected from the real internet environment. Based on FLUB, we investigate the performance of multiple representative and advanced LLMs.
  arXiv  Detail & Related papers  (2024-02-16T22:12:53Z)
• INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning [59.07490387145391]
  Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. Their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. We introduce a novel instruction tuning dataset, INTERS, encompassing 20 tasks across three fundamental IR categories.
  arXiv  Detail & Related papers  (2024-01-12T12:10:28Z)
• Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models [76.99140362751787]
  We present NuInstruct, a novel dataset with 91K multi-view video-QA pairs across 17 subtasks. We also present BEV-InMLLM, an end-to-end method for efficiently deriving instruction-aware Bird's-Eye-View features.
  arXiv  Detail & Related papers  (2024-01-02T01:54:22Z)
• A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
  Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language. This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-09T02:55:58Z)
• BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing [10.698756010878688]
  We created the BioInstruct, comprising 25,005 instructions to instruction-tune large language models (LLMs) The instructions were created by prompting the GPT-4 language model with three-seed samples randomly drawn from an 80 human curated instructions. We evaluated these instruction-tuned LLMs on several BioNLP tasks, which can be grouped into three major categories: question answering(QA), information extraction(IE), and text generation(GEN)
  arXiv  Detail & Related papers  (2023-10-30T19:38:50Z)
• The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition [0.0]
  It is common to have Natural Language Understanding systems limited to a subset of languages due to lack of available data. We launch a three-phase approach to address the limitations in NLU and help propel NLU technology to new heights. We release a 52 language dataset called the Multilingual Amazon SLU resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. We organize the Massively Multilingual NLU 2022 Challenge to provide a competitive environment and push the state-of-the art in the transferability of models into other languages.
  arXiv  Detail & Related papers  (2022-12-13T03:00:36Z)
• NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue [53.54788957697192]
  NLU++ is a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems. NLU++ is divided into two domains (BANKING and HOTELS) and brings several crucial improvements over current commonly used NLU datasets.
  arXiv  Detail & Related papers  (2022-04-27T16:00:23Z)

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.