Efficient Scientific Full Text Classification: The Case of EICAT Impact Assessments

• URL: http://arxiv.org/abs/2502.06551v1
• Date: Mon, 10 Feb 2025 15:19:22 GMT
• Title: Efficient Scientific Full Text Classification: The Case of EICAT Impact Assessments
• Authors: Marc Felix Brinner, Sina Zarrieß,
• Abstract summary: We focus on developing methods for selecting subsets of input sentences to reduce input size.<n>We compile a novel dataset consisting of full-text scientific papers from the field of invasion biology.
• Score: 8.883534683127415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores strategies for efficiently classifying scientific full texts using both small, BERT-based models and local large language models like Llama-3.1 8B. We focus on developing methods for selecting subsets of input sentences to reduce input size while simultaneously enhancing classification performance. To this end, we compile a novel dataset consisting of full-text scientific papers from the field of invasion biology, specifically addressing the impacts of invasive species. These papers are aligned with publicly available impact assessments created by researchers for the International Union for Conservation of Nature (IUCN). Through extensive experimentation, we demonstrate that various sources like human evidence annotations, LLM-generated annotations or explainability scores can be used to train sentence selection models that improve the performance of both encoder- and decoder-based language models while optimizing efficiency through the reduction in input length, leading to improved results even if compared to models like ModernBERT that are able to handle the complete text as input. Additionally, we find that repeated sampling of shorter inputs proves to be a very effective strategy that, at a slightly increased cost, can further improve classification performance.

Related papers

• Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning [6.549601823162279]
  Large Language Models (LLMs) have become a cornerstone in Natural Language Processing (NLP)<n>We explore several adaptation strategies for pre-trained, decoder-only LLMs.
  arXiv  Detail & Related papers  (2025-07-30T14:49:30Z)
• Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer [0.0]
  This paper studies a text classification algorithm based on an improved Transformer to improve the performance and efficiency of the model in text classification tasks.<n>The improved Transformer model outperforms the comparative models such as BiLSTM, CNN, standard Transformer, and BERT in terms of classification accuracy, F1 score, and recall rate.
  arXiv  Detail & Related papers  (2025-01-23T08:32:27Z)
• READ: Reinforcement-based Adversarial Learning for Text Classification with Limited Labeled Data [7.152603583363887]
  Pre-trained transformer models such as BERT have shown massive gains across many text classification tasks.<n>This paper proposes a method that encapsulates reinforcement learning-based text generation and semi-supervised adversarial learning approaches.<n>Our method READ, Reinforcement-based Adversarial learning, utilizes an unlabeled dataset to generate diverse synthetic text through reinforcement learning.
  arXiv  Detail & Related papers  (2025-01-14T11:39:55Z)
• Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts [1.565361244756411]
  Large language models (LLMs) play a crucial role in natural language processing (NLP) tasks. This study applied prompt-based data augmentation to detect mentions of green practices in Russian social media.
  arXiv  Detail & Related papers  (2024-11-22T12:37:41Z)
• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams [49.3179290313959]
  This study explores the efficacy of seven text sampling methods designed to selectively fine-tune language models. We precisely assess the impact of these methods on fine-tuning the SBERT model using four different loss functions. Our findings indicate that Softmax loss and Batch All Triplets loss are particularly effective for text stream classification.
  arXiv  Detail & Related papers  (2024-03-18T23:41:52Z)
• Influence Scores at Scale for Efficient Language Data Sampling [3.072340427031969]
  "influence scores" are used to identify important subsets of data. In this paper, we explore the applicability of influence scores in language classification tasks.
  arXiv  Detail & Related papers  (2023-11-27T20:19:22Z)
• Active Learning for Abstractive Text Summarization [50.79416783266641]
  We propose the first effective query strategy for Active Learning in abstractive text summarization. We show that using our strategy in AL annotation helps to improve the model performance in terms of ROUGE and consistency scores.
  arXiv  Detail & Related papers  (2023-01-09T10:33:14Z)
• Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification [59.698811329287174]
  We leverage GPT-2 for generating artificial training instances in order to improve classification performance. Our results show that fine-tuning GPT-2 in a handful of label instances leads to consistent classification improvements.
  arXiv  Detail & Related papers  (2021-11-17T12:10:03Z)
• Efficient Nearest Neighbor Language Models [114.40866461741795]
  Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore. We show how to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
  arXiv  Detail & Related papers  (2021-09-09T12:32:28Z)
• SDA: Improving Text Generation with Self Data Augmentation [88.24594090105899]
  We propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, our method is more general and could be easily adapted to any MLE-based training procedure.
  arXiv  Detail & Related papers  (2021-01-02T01:15:57Z)

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.