Robustness of GPT Large Language Models on Natural Language Processing Tasks

Chen Xuanting; Ye Junjie; Zu Can; Xu Nuo; Gui Tao; Zhang Qi

doi:10.7544/issn1000-1239.202330801

Journal of Computer Research and Development > 2024 > 61(5): 1128-1142. > DOI: 10.7544/issn1000-1239.202330801 CSTR: 32373.14.issn1000-1239.202330801

Chen Xuanting, Ye Junjie, Zu Can, Xu Nuo, Gui Tao, Zhang Qi. Robustness of GPT Large Language Models on Natural Language Processing Tasks[J]. Journal of Computer Research and Development, 2024, 61(5): 1128-1142. DOI: 10.7544/issn1000-1239.202330801

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Robustness of GPT Large Language Models on Natural Language Processing Tasks

1.
School of Computer Science, Fudan University, Shanghai 200433
2.
Institute of Modern Languages and Linguistics, Fudan University, Shanghai 200433

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Author Bio:
Chen Xuanting: born in 1999. Master candidate. Her main research interests include natural language processing and robust models

Ye Junjie: born in 2001. PhD candidate. His main research interest includes natural language processing

Zu Can: born in 2000. Master candidate. Her main research interests include large language models and information extraction

Xu Nuo: born in 1998. Master candidate. Her main research interests include natural language processing and large language models

Gui Tao: born in 1989. PhD, associate professor, master supervisor. His main research interests include pre-training models, information extraction, and robust models

Zhang Qi: born in 1981. PhD, professor, PhD supervisor. His main research interests include natural language processing and information retrieval
Received Date: October 09, 2023
Revised Date: March 11, 2024
Available Online: March 11, 2024

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Abstract

Abstract

The GPT models have demonstrated impressive performance in various natural language processing (NLP) tasks. However, their robustness and abilities to handle various complexities of the open world have not yet to be well explored, which is especially crucial in assessing the stability of models and is a key aspect of trustworthy AI. In this study, we perform a comprehensive experimental analysis of GPT-3 and GPT-3.5 series models, exploring their performance and robustness using 15 datasets (about 147000 original test samples) with 61 robust probing transformations from TextFlint covering 9 popular NLP tasks. Additionally, we analyze the model’s robustness across different transformation levels, including character, word, and sentence. Our findings reveal that while GPT models exhibit competitive performance in certain tasks like sentiment analysis, semantic matching, and reading comprehension, they exhibit severe confusion regarding information extraction tasks. For instance, GPT models exhibit severe confusion in relation extraction and even exhibit “hallucination” phenomena. Moreover, they experience significant degradation in robustness in terms of tasks and transformations, especially in classification tasks and sentence-level transformations. Furthermore, we validate the impact of the quantity and the form of demonstrations on performance and robustness. These findings reveal that GPT models are still not fully proficient in handling common NLP tasks, and highlight the difficulty in addressing robustness challenges through enhancing model performance or altering prompt content. By comparing the performance and robustness of the updated version of gpt-3.5-turbo, gpt-4, LLaMA2-7B and LLaMA2-13B, we further validate the experimental findings. Future studies on large language models should strive to enhance their capacities in information extraction and semantic understanding, while simultaneously bolstering overall robustness.
- robustness,
- GPT models,
- large language models,
- natural language processing,
- reliability

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References (41)

References

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