Is This Abstract Generated by AI? A Research for the Gap between AI-generated Scientific Text and Human-written Scientific Text

• URL: http://arxiv.org/abs/2301.10416v1
• Date: Tue, 24 Jan 2023 04:23:20 GMT
• Title: Is This Abstract Generated by AI? A Research for the Gap between AI-generated Scientific Text and Human-written Scientific Text
• Authors: Yongqiang Ma, Jiawei Liu, Fan Yi
• Abstract summary: We investigate the gap between scientific content generated by AI and written by humans. We find that there exists a writing style'' gap between AI-generated scientific text and human-written scientific text.
• Score: 13.438933219811188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: BACKGROUND: Recent neural language models have taken a significant step forward in producing remarkably controllable, fluent, and grammatical text. Although some recent works have found that AI-generated text is not distinguishable from human-authored writing for crowd-sourcing workers, there still exist errors in AI-generated text which are even subtler and harder to spot. METHOD: In this paper, we investigate the gap between scientific content generated by AI and written by humans. Specifically, we first adopt several publicly available tools or models to investigate the performance for detecting GPT-generated scientific text. Then we utilize features from writing style to analyze the similarities and differences between the two types of content. Furthermore, more complex and deep perspectives, such as consistency, coherence, language redundancy, and factual errors, are also taken into consideration for in-depth analysis. RESULT: The results suggest that while AI has the potential to generate scientific content that is as accurate as human-written content, there is still a gap in terms of depth and overall quality. AI-generated scientific content is more likely to contain errors in language redundancy and factual issues. CONCLUSION: We find that there exists a ``writing style'' gap between AI-generated scientific text and human-written scientific text. Moreover, based on the analysis result, we summarize a series of model-agnostic or distribution-agnostic features, which could be utilized to unknown or novel domain distribution and different generation methods. Future research should focus on not only improving the capabilities of AI models to produce high-quality content but also examining and addressing ethical and security concerns related to the generation and the use of AI-generated content.

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