In-BoXBART: Get Instructions into Biomedical Multi-Task Learning

• URL: http://arxiv.org/abs/2204.07600v1
• Date: Fri, 15 Apr 2022 18:06:22 GMT
• Title: In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
• Authors: Mihir Parmar, Swaroop Mishra, Mirali Purohit, Man Luo, M. Hassan Murad and Chitta Baral
• Abstract summary: Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications. We introduce the BoX, a collection of 32 instruction tasks for Biomedical NLP across various categories. We propose a unified model termed In-BoXBART, that can jointly learn all tasks of the BoX without any task-specific modules.
• Score: 18.3293060030174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications where multi-tasking is necessary and domain shifts are exhibited. Recently, instructional prompts have shown significant improvement towards multi-task generalization; however, the effect of instructional prompts and Multi-Task Learning (MTL) has not been systematically studied in the biomedical domain. Motivated by this, this paper explores the impact of instructional prompts for biomedical MTL. We introduce the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X) various categories. Using this meta-dataset, we propose a unified model termed In-BoXBART, that can jointly learn all tasks of the BoX without any task-specific modules. To the best of our knowledge, this is the first attempt to propose a unified model in the biomedical domain and use instructions to achieve generalization across several biomedical tasks. Experimental results indicate that the proposed model: 1) outperforms the single-task baseline by ~3% and multi-task (without instruction) baseline by ~18% on an average, and 2) shows ~23% improvement compared to the single-task baseline in few-shot learning (i.e., 32 instances per task) on an average. Our analysis indicates that there is significant room for improvement across tasks in the BoX, implying the scope for future research direction.

Related papers

• Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond [62.406687088097605]
  Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space. We show that MTL can be successful with classification tasks with little, or non-overlapping annotations. We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
  arXiv  Detail & Related papers  (2024-01-02T14:18:11Z)
• Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation [35.87795950781491]
  We demonstrate a unified transformer model specifically designed for multi-modal clinical tasks by incorporating customized instruction tuning. We first compose a multi-task training dataset comprising 13.4 million instruction and ground-truth pairs. We can unify the various vision-intensive tasks in a single training framework with homogeneous model inputs and outputs to increase clinical interpretability in one reading.
  arXiv  Detail & Related papers  (2023-11-02T08:55:48Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis [17.098951643252345]
  Whole slide image (WSI) has been widely used to assist automated diagnosis under the deep learning fields. We present a novel multi-task framework (i.e., MulGT) for WSI analysis by the specially designed Graph-Transformer.
  arXiv  Detail & Related papers  (2023-02-21T10:00:58Z)
• ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization [36.338614215561805]
  Task-agnostic knowledge distillation attempts to address the problem of deploying large pretrained language model in resource-constrained scenarios. We show that we can leverage multi-task learning in task-agnostic distillation to advance the generalization of the resulted student.
  arXiv  Detail & Related papers  (2023-01-09T15:12:50Z)
• BioTABQA: Instruction Learning for Biomedical Table Question Answering [19.66452178704578]
  Table Question Answering (TQA) is an important but under-explored task. None of TQA datasets exist in the biomedical domain where tables are frequently used to present information. BioTABQA can not only be used to teach a model how to answer questions from tables but also evaluate how a model generalizes to unseen questions.
  arXiv  Detail & Related papers  (2022-07-06T03:40:10Z)
• On Steering Multi-Annotations per Sample for Multi-Task Learning [79.98259057711044]
  The study of multi-task learning has drawn great attention from the community. Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously remains to be explored. Previous works attempt to modify the gradients from different tasks. Yet these methods give a subjective assumption of the relationship between tasks, and the modified gradient may be less accurate. In this paper, we introduce Task Allocation(STA), a mechanism that addresses this issue by a task allocation approach, in which each sample is randomly allocated a subset of tasks. For further progress, we propose Interleaved Task Allocation(ISTA) to iteratively allocate all
  arXiv  Detail & Related papers  (2022-03-06T11:57:18Z)
• Active Multi-Task Representation Learning [50.13453053304159]
  We give the first formal study on resource task sampling by leveraging the techniques from active learning. We propose an algorithm that iteratively estimates the relevance of each source task to the target task and samples from each source task based on the estimated relevance.
  arXiv  Detail & Related papers  (2022-02-02T08:23:24Z)
• The Effect of Diversity in Meta-Learning [79.56118674435844]
  Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that task distribution plays a vital role in the model's performance. We study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms.
  arXiv  Detail & Related papers  (2022-01-27T19:39:07Z)
• Towards More Generalizable One-shot Visual Imitation Learning [81.09074706236858]
  A general-purpose robot should be able to master a wide range of tasks and quickly learn a novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches this goal by training an agent with (pairs of) expert demonstrations. We push for a higher level of generalization ability by investigating a more ambitious multi-task setup.
  arXiv  Detail & Related papers  (2021-10-26T05:49:46Z)
• Partly Supervised Multitask Learning [19.64371980996412]
  Experimental results on chest and spine X-ray datasets suggest that our S$4$MTL model significantly outperforms semi-supervised single task, semi/fully-supervised multitask, and fully-supervised single task models. We hypothesize that our proposed model can be effective in tackling limited annotation problems for joint training, not only in medical imaging domains, but also for general-purpose vision tasks.
  arXiv  Detail & Related papers  (2020-05-05T22:42:12Z)

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.