Understanding and Tackling Label Errors in Individual-Level Nature Language Understanding

• URL: http://arxiv.org/abs/2502.13297v1
• Date: Tue, 18 Feb 2025 21:35:46 GMT
• Title: Understanding and Tackling Label Errors in Individual-Level Nature Language Understanding
• Authors: Yunpeng Xiao, Youpeng Zhao, Kai Shu,
• Abstract summary: We propose a new NLU annotation guideline based on individual-level factors. We use this guideline to expand and re-annotate the stance detection and topic-based sentiment analysis datasets. We find that error rates in the samples were as high as 31.7% and 23.3%.
• Score: 20.544691668254416
• License:
• Abstract: Natural language understanding (NLU) is a task that enables machines to understand human language. Some tasks, such as stance detection and sentiment analysis, are closely related to individual subjective perspectives, thus termed individual-level NLU. Previously, these tasks are often simplified to text-level NLU tasks, ignoring individual factors. This not only makes inference difficult and unexplainable but often results in a large number of label errors when creating datasets. To address the above limitations, we propose a new NLU annotation guideline based on individual-level factors. Specifically, we incorporate other posts by the same individual and then annotate individual subjective perspectives after considering all individual posts. We use this guideline to expand and re-annotate the stance detection and topic-based sentiment analysis datasets. We find that error rates in the samples were as high as 31.7\% and 23.3\%. We further use large language models to conduct experiments on the re-annotation datasets and find that the large language models perform well on both datasets after adding individual factors. Both GPT-4o and Llama3-70B can achieve an accuracy greater than 87\% on the re-annotation datasets. We also verify the effectiveness of individual factors through ablation studies. We call on future researchers to add individual factors when creating such datasets. Our re-annotation dataset can be found at https://github.com/24yearsoldstudent/Individual-NLU

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