Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them

• URL: http://arxiv.org/abs/2503.22006v1
• Date: Thu, 27 Mar 2025 21:51:24 GMT
• Title: Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them
• Authors: Marc Brinner, Tarek Al Mustafa, Sina Zarrieß,
• Abstract summary: We investigate the use of LLM-generated data for continual pretraining of encoder models in domains with limited data.<n>We compile a benchmark specifically designed for assessing embedding model performance in invasion biology.<n>Our results demonstrate that this approach achieves a fully automated pipeline for enhancing domain-specific understanding of small encoder models.
• Score: 9.952432291248954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the use of LLM-generated data for continual pretraining of encoder models in specialized domains with limited training data, using the scientific domain of invasion biology as a case study. To this end, we leverage domain-specific ontologies by enriching them with LLM-generated data and pretraining the encoder model as an ontology-informed embedding model for concept definitions. To evaluate the effectiveness of this method, we compile a benchmark specifically designed for assessing model performance in invasion biology. After demonstrating substantial improvements over standard LLM pretraining, we investigate the feasibility of applying the proposed approach to domains without comprehensive ontologies by substituting ontological concepts with concepts automatically extracted from a small corpus of scientific abstracts and establishing relationships between concepts through distributional statistics. Our results demonstrate that this automated approach achieves comparable performance using only a small set of scientific abstracts, resulting in a fully automated pipeline for enhancing domain-specific understanding of small encoder models that is especially suited for application in low-resource settings and achieves performance comparable to masked language modeling pretraining on much larger datasets.

Related papers

• Meta-Learning for Physically-Constrained Neural System Identification [9.417562391585076]
  We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification.<n>We show that the meta-learned models result in improved downstream performance in model-based state estimation in indoor localization and energy systems.
  arXiv  Detail & Related papers  (2025-01-10T18:46:28Z)
• Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models [6.118896920507198]
  This paper introduces an innovative regression framework utilizing large language models (LLMs) for RUL prediction. Experiments on the Turbofan engine's RUL prediction task show that the proposed model surpasses state-of-the-art (SOTA) methods. With minimal target domain data for fine-tuning, the model outperforms SOTA methods trained on full target domain data.
  arXiv  Detail & Related papers  (2024-10-04T04:21:53Z)
• A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models [1.3927943269211591]
  This paper experiments with leveraging in-context learning capabilities of Large Language Models to perform data annotation. We show that by using a few-shot learning strategy with structured prompts and only minimal expert annotation the presented approach can potentially support domain adaptation of a science KG generation model.
  arXiv  Detail & Related papers  (2024-08-05T11:06:36Z)
• VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition [3.4923338594757674]
  Large language models (LLMs) can be used to train a model capable of extracting various types of entities. In this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and datasets. Our model VANER, trained with a small partition of parameters, significantly outperforms previous LLMs-based models and, for the first time, as a model based on LLM, surpasses the majority of conventional state-of-the-art BioNER systems.
  arXiv  Detail & Related papers  (2024-04-27T09:00:39Z)
• Self-Supervised Dataset Distillation for Transfer Learning [77.4714995131992]
  We propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL) We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is textitbiased due to randomness originating from data augmentations or masking. We empirically validate the effectiveness of our method on various applications involving transfer learning.
  arXiv  Detail & Related papers  (2023-10-10T10:48:52Z)
• Evaluating and Explaining Large Language Models for Code Using Syntactic Structures [74.93762031957883]
  This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes. We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
  arXiv  Detail & Related papers  (2023-08-07T18:50:57Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• HaDR: Applying Domain Randomization for Generating Synthetic Multimodal Dataset for Hand Instance Segmentation in Cluttered Industrial Environments [0.0]
  This study uses domain randomization to generate a synthetic RGB-D dataset for training multimodal instance segmentation models. We show that our approach enables the models to outperform corresponding models trained on existing state-of-the-art datasets.
  arXiv  Detail & Related papers  (2023-04-12T13:02:08Z)
• Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study [76.52997424694767]
  We present an in-depth empirical study of keyphrase extraction and keyphrase generation using pre-trained language models. We show that PLMs have competitive high-resource performance and state-of-the-art low-resource performance. Further results show that in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models.
  arXiv  Detail & Related papers  (2022-12-20T13:20:21Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.