AI Through LLMs by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9789819547982
Publisher: Springer Nature Singapore
Published: 2025-03-15T00:00:00+00:00

We performed a one-way ANOVA on SPSS to determine the statistical differences between the pause lengths of various trainee pairs. The F-value was 8.24 (high), and there was a statistical difference between ChatGPT data and Baidu Translate (p = .005) and LanguageX (p = .04). No other statistical differences were found. The data confirms the screen recording of ChatGPT tool users showing constant activity as trainees continuously interact with the tool.

Based on previous research regarding pause-based PE effort (Alves et al., 2016; O’Brien, 2006), we determined that trainees who used ChatGPT as a post-editing tool had a lower cognitive effort than those with traditional MT systems. In other words, they tended to interact with and depend more on ChatGPT than their cognitive abilities. In contrast, LanguageX had the longest pauses, hence the most cognitive effort.

Requests and Prompts

We tallied trainees’ requests for information and prompts (for ChatGPT users) to understand how much trainees depended on different information sources while post-editing. The literature on post-editing efforts indicates that the percentage of time spent on online resources (OR) correlates with PE effort (Witczak, 2021). In addition, numerical indicators have also been used to measure the technical effort made during PE (Zaretskaya et al., 2016).

Based on our analysis, ChatGPT tool users requested more information than traditional MT system users. The pair made 13 and 15 prompts, respectively, i.e., an average of 14 prompts/requests. This was significantly higher than the request made by trainees using traditional MT systems, as indicated in Fig. 7.

Fig. 7Average number of prompts/requests by trainee pairs per MTPE tool used

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