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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2409.09186 |
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| _version_ | 1866909481559916544 |
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| author | Jung, Minseok Zhang, Aurora Fung, May Lee, Junho Liang, Paul Pu |
| author_facet | Jung, Minseok Zhang, Aurora Fung, May Lee, Junho Liang, Paul Pu |
| contents | Large Language Models (LLMs) are transforming writing, reading, teaching, and knowledge retrieval in many academic fields. However, concerns regarding their misuse and erroneous outputs have led to varying degrees of trust in LLMs within academic communities. In response, various academic organizations have proposed and adopted policies regulating their usage. However, these policies are not based on substantial quantitative evidence because there is no data about use patterns and user opinion. Consequently, there is a pressing need to accurately quantify their usage, user trust in outputs, and concerns about key issues to prioritize in deployment. This study addresses these gaps through a quantitative user study of LLM usage and trust in academic research and education. Specifically, our study surveyed 125 individuals at a private R1 research university regarding their usage of LLMs, their trust in LLM outputs, and key issues to prioritize for robust usage in academia. Our findings reveal: (1) widespread adoption of LLMs, with 75% of respondents actively using them; (2) a significant positive correlation between trust and adoption, as well as between engagement and trust; and (3) that fact-checking is the most critical concern. These findings suggest a need for policies that address pervasive usage, prioritize fact-checking mechanisms, and accurately calibrate user trust levels as they engage with these models. These strategies can help balance innovation with accountability and help integrate LLMs into the academic environment effectively and reliably. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantitative Insights into Large Language Model Usage and Trust in Academia: An Empirical Study Jung, Minseok Zhang, Aurora Fung, May Lee, Junho Liang, Paul Pu Computers and Society Large Language Models (LLMs) are transforming writing, reading, teaching, and knowledge retrieval in many academic fields. However, concerns regarding their misuse and erroneous outputs have led to varying degrees of trust in LLMs within academic communities. In response, various academic organizations have proposed and adopted policies regulating their usage. However, these policies are not based on substantial quantitative evidence because there is no data about use patterns and user opinion. Consequently, there is a pressing need to accurately quantify their usage, user trust in outputs, and concerns about key issues to prioritize in deployment. This study addresses these gaps through a quantitative user study of LLM usage and trust in academic research and education. Specifically, our study surveyed 125 individuals at a private R1 research university regarding their usage of LLMs, their trust in LLM outputs, and key issues to prioritize for robust usage in academia. Our findings reveal: (1) widespread adoption of LLMs, with 75% of respondents actively using them; (2) a significant positive correlation between trust and adoption, as well as between engagement and trust; and (3) that fact-checking is the most critical concern. These findings suggest a need for policies that address pervasive usage, prioritize fact-checking mechanisms, and accurately calibrate user trust levels as they engage with these models. These strategies can help balance innovation with accountability and help integrate LLMs into the academic environment effectively and reliably. |
| title | Quantitative Insights into Large Language Model Usage and Trust in Academia: An Empirical Study |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2409.09186 |