ACL Findings2025
The Impact of Large Language Models in Academia: from Writing to Speaking
Mingmeng Geng, Caixi Chen, Yanru Wu, Yao Wan, Pan Zhou, Dongping Chen
Abstract
Large language models (LLMs) are increasingly impacting human society, particularly in textual information. Based on more than 30,000 papers and 1,000 presentations from machine learning conferences, we examined and compared the words used in writing and speaking, representing the first large-scale study of how LLMs influence the two main modes of verbal communication and expression within the same group of people. Our empirical results show that LLM-style words such as "significant" have been used more frequently in abstracts and oral presentations. The implicit impact on human expression like writing and speaking is beginning to emerge and is likely to grow in the future. We take the first step in building an automated monitoring platform to record its longitudinal changes to call attention to the implicit influence and ripple effect of LLMs on human society.
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Builds on11
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- Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defenseKalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting et al.NeurIPS 2023 · 657 citations
- CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model CapabilitiesMina Lee, Percy Liang, Qian YangCHI 2022 · 340 citations
- RADAR: Robust AI-Text Detection via Adversarial LearningXiaomeng Hu, Pin-Yu Chen, Tsung-Yi HoNeurIPS 2023 · 315 citations
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