Selecting Between BERT and GPT for Text Classification in Political Science Research

• URL: http://arxiv.org/abs/2411.05050v1
• Date: Thu, 07 Nov 2024 07:29:39 GMT
• Title: Selecting Between BERT and GPT for Text Classification in Political Science Research
• Authors: Yu Wang, Wen Qu, Xin Ye,
• Abstract summary: We evaluate the effectiveness of BERT-based versus GPT-based models in low-data scenarios. We conclude by comparing these approaches in terms of performance, ease of use, and cost.
• Score: 4.487884986288122
• License:
• Abstract: Political scientists often grapple with data scarcity in text classification. Recently, fine-tuned BERT models and their variants have gained traction as effective solutions to address this issue. In this study, we investigate the potential of GPT-based models combined with prompt engineering as a viable alternative. We conduct a series of experiments across various classification tasks, differing in the number of classes and complexity, to evaluate the effectiveness of BERT-based versus GPT-based models in low-data scenarios. Our findings indicate that while zero-shot and few-shot learning with GPT models provide reasonable performance and are well-suited for early-stage research exploration, they generally fall short - or, at best, match - the performance of BERT fine-tuning, particularly as the training set reaches a substantial size (e.g., 1,000 samples). We conclude by comparing these approaches in terms of performance, ease of use, and cost, providing practical guidance for researchers facing data limitations. Our results are particularly relevant for those engaged in quantitative text analysis in low-resource settings or with limited labeled data.

Related papers

• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• Text generation for dataset augmentation in security classification tasks [55.70844429868403]
  This study evaluates the application of natural language text generators to fill this data gap in multiple security-related text classification tasks. We find substantial benefits for GPT-3 data augmentation strategies in situations with severe limitations on known positive-class samples.
  arXiv  Detail & Related papers  (2023-10-22T22:25:14Z)
• Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance [4.305568120980929]
  In-context learning with GPT-3.5 and GPT-4 minimizes the technical expertise required and eliminates the need for expensive GPU computing. We fine-tune other pre-trained, masked language models with SetFit to achieve state-of-the-art results both in full-data and few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can outperform fine-tuned, non-generative models even with fewer examples.
  arXiv  Detail & Related papers  (2023-08-28T15:04:16Z)
• SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning [5.177947445379688]
  This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations.
  arXiv  Detail & Related papers  (2023-07-16T05:33:35Z)
• On Pitfalls of Test-Time Adaptation [82.8392232222119]
  Test-Time Adaptation (TTA) has emerged as a promising approach for tackling the robustness challenge under distribution shifts. We present TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art algorithms, a diverse array of distribution shifts, and two evaluation protocols.
  arXiv  Detail & Related papers  (2023-06-06T09:35:29Z)
• A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts [143.14128737978342]
  Test-time adaptation, an emerging paradigm, has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions. Recent progress in this paradigm highlights the significant benefits of utilizing unlabeled data for training self-adapted models prior to inference.
  arXiv  Detail & Related papers  (2023-03-27T16:32:21Z)
• Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning [19.7066703371736]
  We propose a novel short text topic modeling framework, Topic-Semantic Contrastive Topic Model (TSCTM) Our TSCTM outperforms state-of-the-art baselines regardless of the data augmentation availability, producing high-quality topics and topic distributions.
  arXiv  Detail & Related papers  (2022-11-23T11:33:43Z)
• 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)
• DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks [88.62288327934499]
  We propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2020-11-03T07:49:15Z)

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.