The Impact of Large Language Models in Academia: from Writing to Speaking

• URL: http://arxiv.org/abs/2409.13686v2
• Date: Tue, 22 Oct 2024 17:06:17 GMT
• Title: The Impact of Large Language Models in Academia: from Writing to Speaking
• Authors: Mingmeng Geng, Caixi Chen, Yanru Wu, Dongping Chen, Yao Wan, Pan Zhou,
• Abstract summary: We examined and compared the words used in writing and speaking based on more than 30,000 papers and 1,000 presentations from machine learning conferences. Our results show that LLM-style words such as "significant" have been used more frequently in abstracts and oral presentations. The impact on speaking is beginning to emerge and is likely to grow in the future, calling attention to the implicit influence and ripple effect of LLMs on human society.
• Score: 42.1505375956748
• License: http://creativecommons.org/licenses/by/4.0/
• 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 impact on speaking is beginning to emerge and is likely to grow in the future, calling attention to the implicit influence and ripple effect of LLMs on human society.

Related papers

• Evaluating Large language models on Understanding Korean indirect Speech acts [0.6757476692230009]
  This study evaluates whether current LLMs can understand the intention of an utterance by considering the given conversational context. proprietary models exhibited relatively higher performance compared to open-source models. Most LLMs, except for Claude3-Opus, demonstrated significantly lower performance in understanding indirect speech acts.
  arXiv  Detail & Related papers  (2025-02-16T04:59:19Z)
• Why Do Speech Language Models Fail to Generate Semantically Coherent Outputs? A Modality Evolving Perspective [23.49276487518479]
  We explore the influence of three key factors separately by transiting the modality from text to speech in an evolving manner. Factor A has a relatively minor impact, factor B influences syntactical and semantic modeling more obviously, and factor C exerts the most significant impact, particularly in the basic lexical modeling.
  arXiv  Detail & Related papers  (2024-12-22T14:59:19Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models [50.40276881893513]
  This study introduces Spoken Stereoset, a dataset specifically designed to evaluate social biases in Speech Large Language Models (SLLMs) By examining how different models respond to speech from diverse demographic groups, we aim to identify these biases. The findings indicate that while most models show minimal bias, some still exhibit slightly stereotypical or anti-stereotypical tendencies.
  arXiv  Detail & Related papers  (2024-08-14T16:55:06Z)
• Delving into ChatGPT usage in academic writing through excess vocabulary [4.58733012283457]
  Large language models (LLMs) like ChatGPT can generate and revise text with human-level performance. We study vocabulary changes in 14 million PubMed abstracts from 2010--2024, and show how the appearance of LLMs led to an abrupt increase in the frequency of certain style words. We show that LLMs have had an unprecedented impact on the scientific literature, surpassing the effect of major world events such as the Covid pandemic.
  arXiv  Detail & Related papers  (2024-06-11T07:16:34Z)
• Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments [0.0]
  Large Language Models (LLMs) are already as persuasive as humans. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments.
  arXiv  Detail & Related papers  (2024-04-14T19:01:20Z)
• Toward Joint Language Modeling for Speech Units and Text [89.32163954508489]
  We explore joint language modeling for speech units and text. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks.
  arXiv  Detail & Related papers  (2023-10-12T20:53:39Z)
• Investigating the Utility of Surprisal from Large Language Models for Speech Synthesis Prosody [4.081433571732691]
  This paper investigates the use of word surprisal, a measure of the predictability of a word in a given context, as a feature to aid speech prosody synthesis. We conduct experiments using a large corpus of English text and large language models (LLMs) of varying sizes. We find that word surprisal and word prominence are moderately correlated, suggesting that they capture related but distinct aspects of language use.
  arXiv  Detail & Related papers  (2023-06-16T12:49:44Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• "Notic My Speech" -- Blending Speech Patterns With Multimedia [65.91370924641862]
  We propose a view-temporal attention mechanism to model both the view dependence and the visemic importance in speech recognition and understanding. Our proposed method outperformed the existing work by 4.99% in terms of the viseme error rate. We show that there is a strong correlation between our model's understanding of multi-view speech and the human perception.
  arXiv  Detail & Related papers  (2020-06-12T06:51:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.