A Comparative Analysis of Transformer-Based Models: GPT-3, GPT-J, T5, and Bert

Abstract

Transformer-based models are widely used in natural language processing (NLP) for their capacity to produce coherent and contextually relevant text. Among the top Transformer models are GPT-3, GPT-J, T5, and BERT. Although these models share a common underlying structure, they vary in distinct characteristics and functionalities. The paper seeks to analyze and evaluate the performance of Transformer-based models in previous studies by discussing the Transformer tools used and their advantages and disadvantages. Engaged in a literature review of studies comparing various models, employing thematic and content analysis for data extraction and interpretation. Based on the literature review, it was found that GPT-3 and T5 showed strong performance in different NLP tasks, with T5 surpassing GPT-3 specifically in text-to-text transfer tasks. However, BERT demonstrated promising outcomes in tasks like sentiment analysis, whereas GPT-J proved to be efficient in tasks related to person attributes. Nevertheless, the literature points out limitations such as bias, pre-trained nature, and lack of interpretability in these models.

Keywords: Transformers, GPT-3, GPT-J, T5, BERT, natural language processing, literature review

Introduction

Transformer-based models have transformed NLP with cutting-edge performance in tasks like language translation, text summarization, and sentiment analysis. Ever since OpenAI introduced GPT-3 in 2020, a variety of other Transformer-based models like GPT-J, T5, and BERT have surfaced, each showcasing distinct characteristics and abilities. Every model comes with distinct features and capabilities, so it’s crucial to compare them to assess their effectiveness in different tasks. Debates have been ignited by the rise of various Transformer-based models regarding their performance compared to each other. To tackle this question, researchers have carried out several studies to evaluate the performance of these models. The purpose of this study is to analyze past research and evaluate Transformer-based models to identify their advantages and drawbacks.

Literature review

Different studies have used various metrics to evaluate the performance of these models. Yang et al. (2023) compared the performance of BERT and GPT in a text summarization task using BLEU and ROUGE metrics and found that BERT outperformed GPT on both metrics. However, they also identified that GPT shows a significant advantage in terms of memory usage and speed compared to BERT. On the other hand, Kalyan (2023) conducted a survey of GPT-3 family large language models, including GPT-J, and found that GPT-3 models have shown significant improvements over previous models, including BERT, in language translation, question-answering, and text completion tasks. T5 has shown considerable success in text-to-text tasks, such as summarization and question-answering. In a quantitative comparison study, Yang et al. (2023) evaluated T5, GPT-2, and BERT’s performance on text summarization tasks and found that T5 outperformed the other models in terms of fluency and accuracy. Furthermore, Bahani et al. (2023) investigated the effectiveness of these models in text-to-image generation tasks and concluded that T5 showed the best results. Comparing the performance of BERT and GPT-J, Su et al. (2023) and Qiu and Jin (2024) conducted a case study to determine which model was better suited for knowledge-grounded response selection in retrieval-based chatbots. They found that BERT-KRS, a BERT-based model, performed better than GPT-J and other models in terms of accuracy and efficiency. Similarly, Chan (2023) compared GPT-3 and InstructGPT in terms of ethical implications and identified how these models could potentially lead to technological utopianism and dystopianism. The strengths identified for Transformer-based models include their ability to handle large amounts of data, their high performance on language tasks, and their transfer learning capabilities. However, these models also have limitations, such as potential biases and ethical concerns, lack of explainability, and unsuitability for certain tasks (Casola et al., 2022). As Transformer-based models continue to evolve, navigating the balance between their potential and ethical concerns remains a critical challenge.

Methodology

To identify relevant studies, we conducted a literature search using databases such as Springer, IEEE Xplore, and ScienceDirect, with keywords such as “Transformer-based models,” “GPT-3,” “GPT-J,” “T5,” and “BERT.” Further research studies were identified by conducting a manual search of the reference lists of pertinent articles. The inclusion criteria for the review were articles that were published between 2020 and 2022 and compared a minimum of two Transformer-based models (GPT-3, GPT-J, T5, BERT). Exclusion criteria encompassed studies that examined a single model or conducted comparisons between transformer-based and non-transformer models. Utilizing a thematic analysis methodology, the data were extracted and analyzed. The findings, research questions investigated, methodologies implemented, and primary models compared were among the data extracted from the chosen studies.

Discussion.

Based on the literature review, it can be observed that previous studies have shown varying results regarding the performance of Transformer-based models. While some studies like Zhang and Li (2021) have reported GPT-3 to be the best-performing model overall, studies by Kaur and Kaur (2023) and Rodriguez-Torrealba et al. (2022). others have found T5 or BERT to outperform the former in specific tasks. The variation can be attributed to the differences in datasets, contexts, and evaluation metrics used in each study. Notably, the strengths of each model identified in the literature were consistent with their initial design purposes. For instance, GPT-3 was designed for natural language generation, while BERT was meant for natural language understanding, as seen in their performance in tasks related to their primary purposes (Zhang & Li, 2021; Goossens et al., 2023). Moreover, the limitations identified in these models include their sensitivity to dataset sizes, contextual information, and computation requirements.

Table 1: Key characteristics and percentages of usage in previous studies

Conclusion

The literature evaluation offers useful information on the efficacy and constraints of various Transformer-based models. There are numerous implications for academics and practitioners in the field of NLP that stem from the study’s findings. Establishing consistent assessment procedures is of the utmost importance to ensure fair and meaningful comparisons among different models. Additionally, future research must prioritize the examination of these models’ applicability and generalizability to diverse languages and contexts. Significant progress has been made in recent years with these models, and their capabilities will undoubtedly be further enhanced as they expand, rendering them indispensable instruments for a variety of NLP applications.

References

Bahani, M., El Ouaazizi, A., & Maalmi, K. (2023). The effectiveness of T5, GPT-2, and BERT on text-to-image generation task. Pattern Recognition Letters, 173, 57-63. https://doi.org/10.1016/j.patrec.2023.08.001

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Chan, A. (2023). GPT-3 and InstructGPT: Technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry. AI and Ethics, 3(1), 53-64. https://link.springer.com/article/10.1007/s43681-022-00148-6

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Rodriguez-Torrealba, R., Garcia-Lopez, E., & Garcia-Cabot, A. (2022). End-to-End generation of Multiple-Choice questions using Text-to-Text transfer Transformer models. Expert Systems with Applications, 208, 118258. https://doi.org/10.1016/j.eswa.2022.118258

Su, J., Yu, S., Ye, X., & Ma, D. (2023, December). BERT-KRS: A BERT-Based Model for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots. In International Conference on Applied Intelligence (pp. 310-321). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-97-0827-7_27

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